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Types of Research – Explained with Examples

DiscoverPhDs

  • By DiscoverPhDs
  • October 2, 2020

Types of Research Design

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

According to its Purpose

Theoretical research.

Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.

Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

Applied Research

Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

  • Technological applied research : looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.
  • Scientific applied research : has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

Methodology Research

According to your Depth of Scope

Exploratory research.

Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

According to the Type of Data Used

Qualitative research.

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.

Types of Research Methodology

According to the Degree of Manipulation of Variables

Experimental research.

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

According to the Type of Inference

Deductive investigation.

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

Descriptive Research Design

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

According to The Sources of Information

Primary research.

This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research

Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.

Action Research Methods

According to How the Data is Obtained

Documentary (cabinet).

Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.

Field research study involves the direct collection of information at the location where the observed phenomenon occurs.

From Laboratory

Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.

Mixed-Method: Documentary, Field and/or Laboratory

Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.

What is a Research Instrument?

The term research instrument refers to any tool that you may use to collect, measure and analyse research data.

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Clinical research study designs: The essentials

Ambika g. chidambaram.

1 Children's Hospital of Philadelphia, Philadelphia Pennsylvania, USA

Maureen Josephson

In clinical research, our aim is to design a study which would be able to derive a valid and meaningful scientific conclusion using appropriate statistical methods. The conclusions derived from a research study can either improve health care or result in inadvertent harm to patients. Hence, this requires a well‐designed clinical research study that rests on a strong foundation of a detailed methodology and governed by ethical clinical principles. The purpose of this review is to provide the readers an overview of the basic study designs and its applicability in clinical research.

Introduction

In clinical research, our aim is to design a study, which would be able to derive a valid and meaningful scientific conclusion using appropriate statistical methods that can be translated to the “real world” setting. 1 Before choosing a study design, one must establish aims and objectives of the study, and choose an appropriate target population that is most representative of the population being studied. The conclusions derived from a research study can either improve health care or result in inadvertent harm to patients. Hence, this requires a well‐designed clinical research study that rests on a strong foundation of a detailed methodology and is governed by ethical principles. 2

From an epidemiological standpoint, there are two major types of clinical study designs, observational and experimental. 3 Observational studies are hypothesis‐generating studies, and they can be further divided into descriptive and analytic. Descriptive observational studies provide a description of the exposure and/or the outcome, and analytic observational studies provide a measurement of the association between the exposure and the outcome. Experimental studies, on the other hand, are hypothesis testing studies. It involves an intervention that tests the association between the exposure and outcome. Each study design is different, and so it would be important to choose a design that would most appropriately answer the question in mind and provide the most valuable information. We will be reviewing each study design in detail (Figure  1 ).

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Overview of clinical research study designs

Observational study designs

Observational studies ask the following questions: what, who, where and when. There are many study designs that fall under the umbrella of descriptive study designs, and they include, case reports, case series, ecologic study, cross‐sectional study, cohort study and case‐control study (Figure  2 ).

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Classification of observational study designs

Case reports and case series

Every now and then during clinical practice, we come across a case that is atypical or ‘out of the norm’ type of clinical presentation. This atypical presentation is usually described as case reports which provides a detailed and comprehensive description of the case. 4 It is one of the earliest forms of research and provides an opportunity for the investigator to describe the observations that make a case unique. There are no inferences obtained and therefore cannot be generalized to the population which is a limitation. Most often than not, a series of case reports make a case series which is an atypical presentation found in a group of patients. This in turn poses the question for a new disease entity and further queries the investigator to look into mechanistic investigative opportunities to further explore. However, in a case series, the cases are not compared to subjects without the manifestations and therefore it cannot determine which factors in the description are unique to the new disease entity.

Ecologic study

Ecological studies are observational studies that provide a description of population group characteristics. That is, it describes characteristics to all individuals within a group. For example, Prentice et al 5 measured incidence of breast cancer and per capita intake of dietary fat, and found a correlation that higher per capita intake of dietary fat was associated with an increased incidence of breast cancer. But the study does not conclude specifically which subjects with breast cancer had a higher dietary intake of fat. Thus, one of the limitations with ecologic study designs is that the characteristics are attributed to the whole group and so the individual characteristics are unknown.

Cross‐sectional study

Cross‐sectional studies are study designs used to evaluate an association between an exposure and outcome at the same time. It can be classified under either descriptive or analytic, and therefore depends on the question being answered by the investigator. Since, cross‐sectional studies are designed to collect information at the same point of time, this provides an opportunity to measure prevalence of the exposure or the outcome. For example, a cross‐sectional study design was adopted to estimate the global need for palliative care for children based on representative sample of countries from all regions of the world and all World Bank income groups. 6 The limitation of cross‐sectional study design is that temporal association cannot be established as the information is collected at the same point of time. If a study involves a questionnaire, then the investigator can ask questions to onset of symptoms or risk factors in relation to onset of disease. This would help in obtaining a temporal sequence between the exposure and outcome. 7

Case‐control study

Case‐control studies are study designs that compare two groups, such as the subjects with disease (cases) to the subjects without disease (controls), and to look for differences in risk factors. 8 This study is used to study risk factors or etiologies for a disease, especially if the disease is rare. Thus, case‐control studies can also be hypothesis testing studies and therefore can suggest a causal relationship but cannot prove. It is less expensive and less time‐consuming than cohort studies (described in section “Cohort study”). An example of a case‐control study was performed in Pakistan evaluating the risk factors for neonatal tetanus. They retrospectively reviewed a defined cohort for cases with and without neonatal tetanus. 9 They found a strong association of the application of ghee (clarified butter) as a risk factor for neonatal tetanus. Although this suggests a causal relationship, cause cannot be proven by this methodology (Figure  3 ).

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Case‐control study design

One of the limitations of case‐control studies is that they cannot estimate prevalence of a disease accurately as a proportion of cases and controls are studied at a time. Case‐control studies are also prone to biases such as recall bias, as the subjects are providing information based on their memory. Hence, the subjects with disease are likely to remember the presence of risk factors compared to the subjects without disease.

One of the aspects that is often overlooked is the selection of cases and controls. It is important to select the cases and controls appropriately to obtain a meaningful and scientifically sound conclusion and this can be achieved by implementing matching. Matching is defined by Gordis et al as ‘the process of selecting the controls so that they are similar to the cases in certain characteristics such as age, race, sex, socioeconomic status and occupation’ 7 This would help identify risk factors or probable etiologies that are not due to differences between the cases and controls.

Cohort study

Cohort studies are study designs that compare two groups, such as the subjects with exposure/risk factor to the subjects without exposure/risk factor, for differences in incidence of outcome/disease. Most often, cohort study designs are used to study outcome(s) from a single exposure/risk factor. Thus, cohort studies can also be hypothesis testing studies and can infer and interpret a causal relationship between an exposure and a proposed outcome, but cannot establish it (Figure  4 ).

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Cohort study design

Cohort studies can be classified as prospective and retrospective. 7 Prospective cohort studies follow subjects from presence of risk factors/exposure to development of disease/outcome. This could take up to years before development of disease/outcome, and therefore is time consuming and expensive. On the other hand, retrospective cohort studies identify a population with and without the risk factor/exposure based on past records and then assess if they had developed the disease/outcome at the time of study. Thus, the study design for prospective and retrospective cohort studies are similar as we are comparing populations with and without exposure/risk factor to development of outcome/disease.

Cohort studies are typically chosen as a study design when the suspected exposure is known and rare, and the incidence of disease/outcome in the exposure group is suspected to be high. The choice between prospective and retrospective cohort study design would depend on the accuracy and reliability of the past records regarding the exposure/risk factor.

Some of the biases observed with cohort studies include selection bias and information bias. Some individuals who have the exposure may refuse to participate in the study or would be lost to follow‐up, and in those instances, it becomes difficult to interpret the association between an exposure and outcome. Also, if the information is inaccurate when past records are used to evaluate for exposure status, then again, the association between the exposure and outcome becomes difficult to interpret.

Case‐control studies based within a defined cohort

Case‐control studies based within a defined cohort is a form of study design that combines some of the features of a cohort study design and a case‐control study design. When a defined cohort is embedded in a case‐control study design, all the baseline information collected before the onset of disease like interviews, surveys, blood or urine specimens, then the cohort is followed onset of disease. One of the advantages of following the above design is that it eliminates recall bias as the information regarding risk factors is collected before onset of disease. Case‐control studies based within a defined cohort can be further classified into two types: Nested case‐control study and Case‐cohort study.

Nested case‐control study

A nested case‐control study consists of defining a cohort with suspected risk factors and assigning a control within a cohort to the subject who develops the disease. 10 Over a period, cases and controls are identified and followed as per the investigator's protocol. Hence, the case and control are matched on calendar time and length of follow‐up. When this study design is implemented, it is possible for the control that was selected early in the study to develop the disease and become a case in the latter part of the study.

Case‐cohort Study

A case‐cohort study is similar to a nested case‐control study except that there is a defined sub‐cohort which forms the groups of individuals without the disease (control), and the cases are not matched on calendar time or length of follow‐up with the control. 11 With these modifications, it is possible to compare different disease groups with the same sub‐cohort group of controls and eliminates matching between the case and control. However, these differences will need to be accounted during analysis of results.

Experimental study design

The basic concept of experimental study design is to study the effect of an intervention. In this study design, the risk factor/exposure of interest/treatment is controlled by the investigator. Therefore, these are hypothesis testing studies and can provide the most convincing demonstration of evidence for causality. As a result, the design of the study requires meticulous planning and resources to provide an accurate result.

The experimental study design can be classified into 2 groups, that is, controlled (with comparison) and uncontrolled (without comparison). 1 In the group without controls, the outcome is directly attributed to the treatment received in one group. This fails to prove if the outcome was truly due to the intervention implemented or due to chance. This can be avoided if a controlled study design is chosen which includes a group that does not receive the intervention (control group) and a group that receives the intervention (intervention/experiment group), and therefore provide a more accurate and valid conclusion.

Experimental study designs can be divided into 3 broad categories: clinical trial, community trial, field trial. The specifics of each study design are explained below (Figure  5 ).

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Experimental study designs

Clinical trial

Clinical trials are also known as therapeutic trials, which involve subjects with disease and are placed in different treatment groups. It is considered a gold standard approach for epidemiological research. One of the earliest clinical trial studies was performed by James Lind et al in 1747 on sailors with scurvy. 12 Lind divided twelve scorbutic sailors into six groups of two. Each group received the same diet, in addition to a quart of cider (group 1), twenty‐five drops of elixir of vitriol which is sulfuric acid (group 2), two spoonfuls of vinegar (group 3), half a pint of seawater (group 4), two oranges and one lemon (group 5), and a spicy paste plus a drink of barley water (group 6). The group who ate two oranges and one lemon had shown the most sudden and visible clinical effects and were taken back at the end of 6 days as being fit for duty. During Lind's time, this was not accepted but was shown to have similar results when repeated 47 years later in an entire fleet of ships. Based on the above results, in 1795 lemon juice was made a required part of the diet of sailors. Thus, clinical trials can be used to evaluate new therapies, such as new drug or new indication, new drug combination, new surgical procedure or device, new dosing schedule or mode of administration, or a new prevention therapy.

While designing a clinical trial, it is important to select the population that is best representative of the general population. Therefore, the results obtained from the study can be generalized to the population from which the sample population was selected. It is also as important to select appropriate endpoints while designing a trial. Endpoints need to be well‐defined, reproducible, clinically relevant and achievable. The types of endpoints include continuous, ordinal, rates and time‐to‐event, and it is typically classified as primary, secondary or tertiary. 2 An ideal endpoint is a purely clinical outcome, for example, cure/survival, and thus, the clinical trials will become very long and expensive trials. Therefore, surrogate endpoints are used that are biologically related to the ideal endpoint. Surrogate endpoints need to be reproducible, easily measured, related to the clinical outcome, affected by treatment and occurring earlier than clinical outcome. 2

Clinical trials are further divided into randomized clinical trial, non‐randomized clinical trial, cross‐over clinical trial and factorial clinical trial.

Randomized clinical trial

A randomized clinical trial is also known as parallel group randomized trials or randomized controlled trials. Randomized clinical trials involve randomizing subjects with similar characteristics to two groups (or multiple groups): the group that receives the intervention/experimental therapy and the other group that received the placebo (or standard of care). 13 This is typically performed by using a computer software, manually or by other methods. Hence, we can measure the outcomes and efficacy of the intervention/experimental therapy being studied without bias as subjects have been randomized to their respective groups with similar baseline characteristics. This type of study design is considered gold standard for epidemiological research. However, this study design is generally not applicable to rare and serious disease process as it would unethical to treat that group with a placebo. Please see section “Randomization” for detailed explanation regarding randomization and placebo.

Non‐randomized clinical trial

A non‐randomized clinical trial involves an approach to selecting controls without randomization. With this type of study design a pattern is usually adopted, such as, selection of subjects and controls on certain days of the week. Depending on the approach adopted, the selection of subjects becomes predictable and therefore, there is bias with regards to selection of subjects and controls that would question the validity of the results obtained.

Historically controlled studies can be considered as a subtype of non‐randomized clinical trial. In this study design subtype, the source of controls is usually adopted from the past, such as from medical records and published literature. 1 The advantages of this study design include being cost‐effective, time saving and easily accessible. However, since this design depends on already collected data from different sources, the information obtained may not be accurate, reliable, lack uniformity and/or completeness as well. Though historically controlled studies maybe easier to conduct, the disadvantages will need to be taken into account while designing a study.

Cross‐over clinical trial

In cross‐over clinical trial study design, there are two groups who undergoes the same intervention/experiment at different time periods of the study. That is, each group serves as a control while the other group is undergoing the intervention/experiment. 14 Depending on the intervention/experiment, a ‘washout’ period is recommended. This would help eliminate residuals effects of the intervention/experiment when the experiment group transitions to be the control group. Hence, the outcomes of the intervention/experiment will need to be reversible as this type of study design would not be possible if the subject is undergoing a surgical procedure.

Factorial trial

A factorial trial study design is adopted when the researcher wishes to test two different drugs with independent effects on the same population. Typically, the population is divided into 4 groups, the first with drug A, the second with drug B, the third with drug A and B, and the fourth with neither drug A nor drug B. The outcomes for drug A are compared to those on drug A, drug A and B and to those who were on drug B and neither drug A nor drug B. 15 The advantages of this study design that it saves time and helps to study two different drugs on the same study population at the same time. However, this study design would not be applicable if either of the drugs or interventions overlaps with each other on modes of action or effects, as the results obtained would not attribute to a particular drug or intervention.

Community trial

Community trials are also known as cluster‐randomized trials, involve groups of individuals with and without disease who are assigned to different intervention/experiment groups. Hence, groups of individuals from a certain area, such as a town or city, or a certain group such as school or college, will undergo the same intervention/experiment. 16 Hence, the results will be obtained at a larger scale; however, will not be able to account for inter‐individual and intra‐individual variability.

Field trial

Field trials are also known as preventive or prophylactic trials, and the subjects without the disease are placed in different preventive intervention groups. 16 One of the hypothetical examples for a field trial would be to randomly assign to groups of a healthy population and to provide an intervention to a group such as a vitamin and following through to measure certain outcomes. Hence, the subjects are monitored over a period of time for occurrence of a particular disease process.

Overview of methodologies used within a study design

Randomization.

Randomization is a well‐established methodology adopted in research to prevent bias due to subject selection, which may impact the result of the intervention/experiment being studied. It is one of the fundamental principles of an experimental study designs and ensures scientific validity. It provides a way to avoid predicting which subjects are assigned to a certain group and therefore, prevent bias on the final results due to subject selection. This also ensures comparability between groups as most baseline characteristics are similar prior to randomization and therefore helps to interpret the results regarding the intervention/experiment group without bias.

There are various ways to randomize and it can be as simple as a ‘flip of a coin’ to use computer software and statistical methods. To better describe randomization, there are three types of randomization: simple randomization, block randomization and stratified randomization.

Simple randomization

In simple randomization, the subjects are randomly allocated to experiment/intervention groups based on a constant probability. That is, if there are two groups A and B, the subject has a 0.5 probability of being allocated to either group. This can be performed in multiple ways, and one of which being as simple as a ‘flip of a coin’ to using random tables or numbers. 17 The advantage of using this methodology is that it eliminates selection bias. However, the disadvantage with this methodology is that an imbalance in the number allocated to each group as well as the prognostic factors between groups. Hence, it is more challenging in studies with a small sample size.

Block randomization

In block randomization, the subjects of similar characteristics are classified into blocks. The aim of block randomization is to balance the number of subjects allocated to each experiment/intervention group. For example, let's assume that there are four subjects in each block, and two of the four subjects in each block will be randomly allotted to each group. Therefore, there will be two subjects in one group and two subjects in the other group. 17 The disadvantage with this methodology is that there is still a component of predictability in the selection of subjects and the randomization of prognostic factors is not performed. However, it helps to control the balance between the experiment/intervention groups.

Stratified randomization

In stratified randomization, the subjects are defined based on certain strata, which are covariates. 18 For example, prognostic factors like age can be considered as a covariate, and then the specified population can be randomized within each age group related to an experiment/intervention group. The advantage with this methodology is that it enables comparability between experiment/intervention groups and thus makes result analysis more efficient. But, with this methodology the covariates will need to be measured and determined before the randomization process. The sample size will help determine the number of strata that would need to be chosen for a study.

Blinding is a methodology adopted in a study design to intentionally not provide information related to the allocation of the groups to the subject participants, investigators and/or data analysts. 19 The purpose of blinding is to decrease influence associated with the knowledge of being in a particular group on the study result. There are 3 forms of blinding: single‐blinded, double‐blinded and triple‐blinded. 1 In single‐blinded studies, otherwise called as open‐label studies, the subject participants are not revealed which group that they have been allocated to. However, the investigator and data analyst will be aware of the allocation of the groups. In double‐blinded studies, both the study participants and the investigator will be unaware of the group to which they were allocated to. Double‐blinded studies are typically used in clinical trials to test the safety and efficacy of the drugs. In triple‐blinded studies, the subject participants, investigators and data analysts will not be aware of the group allocation. Thus, triple‐blinded studies are more difficult and expensive to design but the results obtained will exclude confounding effects from knowledge of group allocation.

Blinding is especially important in studies where subjective response are considered as outcomes. This is because certain responses can be modified based on the knowledge of the experiment group that they are in. For example, a group allocated in the non‐intervention group may not feel better as they are not getting the treatment, or an investigator may pay more attention to the group receiving treatment, and thereby potentially affecting the final results. However, certain treatments cannot be blinded such as surgeries or if the treatment group requires an assessment of the effect of intervention such as quitting smoking.

Placebo is defined in the Merriam‐Webster dictionary as ‘an inert or innocuous substance used especially in controlled experiments testing the efficacy of another substance (such as drug)’. 20 A placebo is typically used in a clinical research study to evaluate the safety and efficacy of a drug/intervention. This is especially useful if the outcome measured is subjective. In clinical drug trials, a placebo is typically a drug that resembles the drug to be tested in certain characteristics such as color, size, shape and taste, but without the active substance. This helps to measure effects of just taking the drug, such as pain relief, compared to the drug with the active substance. If the effect is positive, for example, improvement in mood/pain, then it is called placebo effect. If the effect is negative, for example, worsening of mood/pain, then it is called nocebo effect. 21

The ethics of placebo‐controlled studies is complex and remains a debate in the medical research community. According to the Declaration of Helsinki on the use of placebo released in October 2013, “The benefits, risks, burdens and effectiveness of a new intervention must be tested against those of the best proven intervention(s), except in the following circumstances:

Where no proven intervention exists, the use of placebo, or no intervention, is acceptable; or

Where for compelling and scientifically sound methodological reasons the use of any intervention less effective than the best proven one, the use of placebo, or no intervention is necessary to determine the efficacy or safety of an intervention and the patients who receive any intervention less effective than the best proven one, placebo, or no intervention will not be subject to additional risks of serious or irreversible harm as a result of not receiving the best proven intervention.

Extreme care must be taken to avoid abuse of this option”. 22

Hence, while designing a research study, both the scientific validity and ethical aspects of the study will need to be thoroughly evaluated.

Bias has been defined as “any systematic error in the design, conduct or analysis of a study that results in a mistaken estimate of an exposure's effect on the risk of disease”. 23 There are multiple types of biases and so, in this review we will focus on the following types: selection bias, information bias and observer bias. Selection bias is when a systematic error is committed while selecting subjects for the study. Selection bias will affect the external validity of the study if the study subjects are not representative of the population being studied and therefore, the results of the study will not be generalizable. Selection bias will affect the internal validity of the study if the selection of study subjects in each group is influenced by certain factors, such as, based on the treatment of the group assigned. One of the ways to decrease selection bias is to select the study population that would representative of the population being studied, or to randomize (discussed in section “Randomization”).

Information bias is when a systematic error is committed while obtaining data from the study subjects. This can be in the form of recall bias when subject is required to remember certain events from the past. Typically, subjects with the disease tend to remember certain events compared to subjects without the disease. Observer bias is a systematic error when the study investigator is influenced by the certain characteristics of the group, that is, an investigator may pay closer attention to the group receiving the treatment versus the group not receiving the treatment. This may influence the results of the study. One of the ways to decrease observer bias is to use blinding (discussed in section “Blinding”).

Thus, while designing a study it is important to take measure to limit bias as much as possible so that the scientific validity of the study results is preserved to its maximum.

Overview of drug development in the United States of America

Now that we have reviewed the various clinical designs, clinical trials form a major part in development of a drug. In the United States, the Food and Drug Administration (FDA) plays an important role in getting a drug approved for clinical use. It includes a robust process that involves four different phases before a drug can be made available to the public. Phase I is conducted to determine a safe dose. The study subjects consist of normal volunteers and/or subjects with disease of interest, and the sample size is typically small and not more than 30 subjects. The primary endpoint consists of toxicity and adverse events. Phase II is conducted to evaluate of safety of dose selected in Phase I, to collect preliminary information on efficacy and to determine factors to plan a randomized controlled trial. The study subjects consist of subjects with disease of interest and the sample size is also small but more that Phase I (40–100 subjects). The primary endpoint is the measure of response. Phase III is conducted as a definitive trial to prove efficacy and establish safety of a drug. Phase III studies are randomized controlled trials and depending on the drug being studied, it can be placebo‐controlled, equivalence, superiority or non‐inferiority trials. The study subjects consist of subjects with disease of interest, and the sample size is typically large but no larger than 300 to 3000. Phase IV is performed after a drug is approved by the FDA and it is also called the post‐marketing clinical trial. This phase is conducted to evaluate new indications, to determine safety and efficacy in long‐term follow‐up and new dosing regimens. This phase helps to detect rare adverse events that would not be picked up during phase III studies and decrease in the delay in the release of the drug in the market. Hence, this phase depends heavily on voluntary reporting of side effects and/or adverse events by physicians, non‐physicians or drug companies. 2

We have discussed various clinical research study designs in this comprehensive review. Though there are various designs available, one must consider various ethical aspects of the study. Hence, each study will require thorough review of the protocol by the institutional review board before approval and implementation.

CONFLICT OF INTEREST

Chidambaram AG, Josephson M. Clinical research study designs: The essentials . Pediatr Invest . 2019; 3 :245‐252. 10.1002/ped4.12166 [ CrossRef ] [ Google Scholar ]

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1.9: Types of Research Studies and How To Interpret Them

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  • Alice Callahan, Heather Leonard, & Tamberly Powell
  • Lane Community College via OpenOregon

The field of nutrition is dynamic, and our understanding and practices are always evolving. Nutrition scientists are continuously conducting new research and publishing their findings in peer-reviewed journals. This adds to scientific knowledge, but it’s also of great interest to the public, so nutrition research often shows up in the news and other media sources. You might be interested in nutrition research to inform your own eating habits, or if you work in a health profession, so that you can give evidence-based advice to others. Making sense of science requires that you understand the types of research studies used and their limitations.

The Hierarchy of Nutrition Evidence

Researchers use many different types of study designs depending on the question they are trying to answer, as well as factors such as time, funding, and ethical considerations. The study design affects how we interpret the results and the strength of the evidence as it relates to real-life nutrition decisions. It can be helpful to think about the types of studies within a pyramid representing a hierarchy of evidence, where  the  studies at the bottom of the pyramid usually give us the weakest evidence with the least relevance to real-life nutrition decisions, and the studies at the top offer the strongest evidence, with the most relevance to real-life nutrition  decisions .

types of research studies slideshare

The pyramid also represents a few other general ideas. There tend to be more studies published using the methods at the bottom of the pyramid, because they require less time, money, and other resources. When researchers want to test a new hypothesis , they often start with the study designs at the bottom of the pyramid , such as in vitro, animal, or observational studies. Intervention studies are more expensive and resource-intensive, so there are fewer of these types of studies conducted. But they also give us higher quality evidence, so they’re an important next step if observational and non-human studies have shown promising results. Meta-analyses and systematic reviews combine the results of many studies already conducted, so they help researchers summarize scientific knowledge on a topic.

Non-Human Studies: In Vitro & Animal Studies

The simplest form of nutrition research is an in vitro study . In vitro means “within glass,” (although plastic is used more commonly today) and these experiments are conducted within flasks, dishes, plates, and test tubes. These studies are performed on isolated cells or tissue samples, so they’re less expensive and time-intensive than animal or human studies. In vitro studies are vital for zooming in on biological mechanisms, to see how things work at the cellular or molecular level. However, these studies shouldn’t be used to draw conclusions about how things work in humans (or even animals), because we can’t assume that the results will apply to a whole, living organism.

Two photos representing lab research. At left, a person appearing to be a woman with long dark hair and dark skin handles tiny tubes in a black bucket of ice. More tubes surround the bucket on the table. At right, a white mouse with red eyes peers out of an opening of a cage.

Animal studies are one form of in vivo research, which translates to “within the living.” Rats and mice are the most common animals used in nutrition research. Animals are often used in research that would be unethical to conduct in humans. Another advantage of animal dietary studies is that researchers can control exactly what the animals eat. In human studies, researchers can tell subjects what to eat and even provide them with the food, but they may not stick to the planned diet. People are also not very good at estimating, recording, or reporting what they eat and in what quantities. In addition, animal studies typically do not cost as much as human studies.

There are some important limitations of animal research. First, an animal’s metabolism and physiology are different from humans. Plus, animal models of disease (cancer, cardiovascular disease, etc.), although similar, are different from human diseases. Animal research is considered preliminary, and while it can be very important to the process of building scientific understanding and informing the types of studies that should be conducted in humans, animal studies shouldn’t be considered relevant to real-life decisions about how people eat.

Observational Studies

Observational studies  in human nutrition collect information on people’s dietary patterns or nutrient intake and look for associations with health outcomes. Observational studies do not give participants a treatment or intervention; instead, they look at what they’re already doing and see how it relates to their health. These types of study designs can only identify  correlations  (relationships) between nutrition and health; they can’t show that one factor  causes  another. (For that, we need intervention studies, which we’ll discuss in a moment.) Observational studies that describe factors correlated with human health are also called  epidemiological studies . 1

One example of a nutrition hypothesis that has been investigated using observational studies is that eating a Mediterranean diet reduces the risk of developing cardiovascular disease. (A Mediterranean diet focuses on whole grains, fruits and vegetables, beans and other legumes, nuts, olive oil, herbs, and spices. It includes small amounts of animal protein (mostly fish), dairy, and red wine. 2 ) There are three main types of observational studies, all of which could be used to test hypotheses about the Mediterranean diet:

  • Cohort studies follow a group of people (a cohort) over time, measuring factors such as diet and health outcomes. A cohort study of the Mediterranean diet would ask a group of people to describe their diet, and then researchers would track them over time to see if those eating a Mediterranean diet had a lower incidence of cardiovascular disease.
  • Case-control studies compare a group of cases and controls, looking for differences between the two groups that might explain their different health outcomes. For example, researchers might compare a group of people with cardiovascular disease with a group of healthy controls to see whether there were more controls or cases that followed a Mediterranean diet.
  • Cross-sectional studies collect information about a population of people at one point in time. For example, a cross-sectional study might compare the dietary patterns of people from different countries to see if diet correlates with the prevalence of cardiovascular disease in the different countries.

Prospective cohort studies, which enroll a cohort and follow them into the future, are usually considered the strongest type of observational study design. Retrospective studies look at what happened in the past, and they’re considered weaker because they rely on people’s memory of what they ate or how they felt in the past. There are several well-known examples of prospective cohort studies that have described important correlations between diet and disease:

  • Framingham Heart Study : Beginning in 1948, this study has followed the residents of Framingham, Massachusetts to identify risk factors for heart disease.
  • Health Professionals Follow-Up Study : This study started in 1986 and enrolled 51,529 male health professionals (dentists, pharmacists, optometrists, osteopathic physicians, podiatrists, and veterinarians), who complete diet questionnaires every 2 years.
  • Nurses Health Studies : Beginning in 1976, these studies have enrolled three large cohorts of nurses with a total of 280,000 participants. Participants have completed detailed questionnaires about diet, other lifestyle factors (smoking and exercise, for example), and health outcomes.

Observational studies have the advantage of allowing researchers to study large groups of people in the real world, looking at the frequency and pattern of health outcomes and identifying factors that correlate with them. But even very large observational studies may not apply to the population as a whole. For example, the Health Professionals Follow-Up Study and the Nurses Health Studies include people with above-average knowledge of health. In many ways, this makes them ideal study subjects, because they may be more motivated to be part of the study and to fill out detailed questionnaires for years. However, the findings of these studies may not apply to people with less baseline knowledge of health.

We’ve already mentioned another important limitation of observational studies—that they can only determine correlation, not causation. A prospective cohort study that finds that people eating a Mediterranean diet have a lower incidence of heart disease can only show that the Mediterranean diet is correlated with lowered risk of heart disease. It can’t show that the Mediterranean diet directly prevents heart disease. Why? There are a huge number of factors that determine health outcomes such as heart disease, and other factors might explain a correlation found in an observational study. For example, people who eat a Mediterranean diet might also be the same kind of people who exercise more, sleep more, have higher income (fish and nuts can be expensive!), or be less stressed. These are called confounding factors ; they’re factors that can affect the outcome in question (i.e., heart disease) and also vary with the factor being studied (i.e., Mediterranean diet).

Intervention Studies

Intervention studies , also sometimes called experimental studies or clinical trials, include some type of treatment or change imposed by the researcher. Examples of interventions in nutrition research include asking participants to change their diet, take a supplement, or change the time of day that they eat. Unlike observational studies, intervention studies can provide evidence of cause and effect , so they are higher in the hierarchy of evidence pyramid.

The gold standard for intervention studies is the randomized controlled trial (RCT) . In an RCT, study subjects are recruited to participate in the study. They are then randomly assigned into one of at least two groups, one of which is a control group (this is what makes the study controlled ). In an RCT to study the effects of the Mediterranean diet on cardiovascular disease development, researchers might ask the control group to follow a low-fat diet (typically recommended for heart disease prevention) and the intervention group to eat a Mediterrean diet. The study would continue for a defined period of time (usually years to study an outcome like heart disease), at which point the researchers would analyze their data to see if more people in the control or Mediterranean diet had heart attacks or strokes. Because the treatment and control groups were randomly assigned, they should be alike in every other way except for diet, so differences in heart disease could be attributed to the diet. This eliminates the problem of confounding factors found in observational research, and it’s why RCTs can provide evidence of causation, not just correlation.

Imagine for a moment what would happen if the two groups weren’t randomly assigned. What if the researchers let study participants choose which diet they’d like to adopt for the study? They might, for whatever reason, end up with more overweight people who smoke and have high blood pressure in the low-fat diet group, and more people who exercised regularly and had already been eating lots of olive oil and nuts for years in the Mediterranean diet group. If they found that the Mediterranean diet group had fewer heart attacks by the end of the study, they would have no way of knowing if this was because of the diet or because of the underlying differences in the groups. In other words, without randomization, their results would be compromised by confounding factors, with many of the same limitations as observational studies.

In an RCT of a supplement, the control group would receive a placebo—a  “fake” treatment that contains no active ingredients, such as a sugar pill. The use of a placebo is necessary in medical research because of a phenomenon known as the placebo effect. The placebo effect results in a beneficial effect because of a subject’s belief in the treatment, even though there is no treatment actually being administered.

A cartoon depicts the study described in the text. At left is shown the "super duper sports drink" (sports drink plus food coloring) in orange. At right is the regular sports drink in green. A cartoon guy with yellow hair is pictured sprinting. The time with the super duper sports drink is 10.50 seconds, and the time with the regular sports drink is 11.00 seconds. The image reads "the improvement is the placebo effect."

Blinding is a technique to prevent bias in intervention studies. In a study without blinding, the subject and the researchers both know what treatment the subject is receiving. This can lead to bias if the subject or researcher have expectations about the treatment working, so these types of trials are used less frequently. It’s best if a study is double-blind , meaning that neither the researcher nor the subject know what treatment the subject is receiving. It’s relatively simple to double-blind a study where subjects are receiving a placebo or treatment pill, because they could be formulated to look and taste the same. In a single-blind study , either the researcher or the subject knows what treatment they’re receiving, but not both. Studies of diets—such as the Mediterranean diet example—often can’t be double-blinded because the study subjects know whether or not they’re eating a lot of olive oil and nuts. However, the researchers who are checking participants’ blood pressure or evaluating their medical records could be blinded to their treatment group, reducing the chance of bias.

Like all studies, RCTs and other intervention studies do have some limitations. They can be difficult to carry on for long periods of time and require that participants remain compliant with the intervention. They’re also costly and often have smaller sample sizes. Furthermore, it is unethical to study certain interventions. (An example of an unethical intervention would be to advise one group of pregnant mothers to drink alcohol to determine its effects on pregnancy outcomes, because we know that alcohol consumption during pregnancy damages the developing fetus.)

VIDEO: “ Not all scientific studies are created equal ” by David H. Schwartz, YouTube (April 28, 2014), 4:26.

Meta-Analyses and Systematic Reviews

At the top of the hierarchy of evidence pyramid are systematic reviews and meta-analyses .  You can think of these as “studies of studies.” They attempt to combine all of the relevant studies that have been conducted on a research question and summarize their overall conclusions. Researchers conducting a  systematic review  formulate a research question and then systematically and independently identify, select, evaluate, and synthesize all high-quality evidence that relates to the research question. Since systematic reviews combine the results of many studies, they help researchers produce more reliable findings. A  meta-analysis  is a type of systematic review that goes one step further, combining the data from multiple studies and using statistics to summarize it, as if creating a mega-study from many smaller studies . 4

However, even systematic reviews and meta-analyses aren’t the final word on scientific questions. For one thing, they’re only as good as the studies that they include. The  Cochrane Collaboration  is an international consortium of researchers who conduct systematic reviews in order to inform evidence-based healthcare, including nutrition, and their reviews are among the most well-regarded and rigorous in science. For the most recent Cochrane review of the Mediterranean diet and cardiovascular disease, two authors independently reviewed studies published on this question. Based on their inclusion criteria, 30 RCTs with a total of 12,461 participants were included in the final analysis. However, after evaluating and combining the data, the authors concluded that “despite the large number of included trials, there is still uncertainty regarding the effects of a Mediterranean‐style diet on cardiovascular disease occurrence and risk factors in people both with and without cardiovascular disease already.” Part of the reason for this uncertainty is that different trials found different results, and the quality of the studies was low to moderate. Some had problems with their randomization procedures, for example, and others were judged to have unreliable data. That doesn’t make them useless, but it adds to the uncertainty about this question, and uncertainty pushes the field forward towards more and better studies. The Cochrane review authors noted that they found seven ongoing trials of the Mediterranean diet, so we can hope that they’ll add more clarity to this question in the future. 5

Science is an ongoing process. It’s often a slow process, and it contains a lot of uncertainty, but it’s our best method of building knowledge of how the world and human life works. Many different types of studies can contribute to scientific knowledge. None are perfect—all have limitations—and a single study is never the final word on a scientific question. Part of what advances science is that researchers are constantly checking each other’s work, asking how it can be improved and what new questions it raises.

Attributions:

  • “Chapter 1: The Basics” from Lindshield, B. L. Kansas State University Human Nutrition (FNDH 400) Flexbook. goo.gl/vOAnR , CC BY-NC-SA 4.0
  • “ The Broad Role of Nutritional Science ,” section 1.3 from the book An Introduction to Nutrition (v. 1.0), CC BY-NC-SA 3.0

References:

  • 1 Thiese, M. S. (2014). Observational and interventional study design types; an overview. Biochemia Medica , 24 (2), 199–210. https://doi.org/10.11613/BM.2014.022
  • 2 Harvard T.H. Chan School of Public Health. (2018, January 16). Diet Review: Mediterranean Diet . The Nutrition Source. https://www.hsph.harvard.edu/nutritionsource/healthy-weight/diet-reviews/mediterranean-diet/
  • 3 Ross, R., Gray, C. M., & Gill, J. M. R. (2015). Effects of an Injected Placebo on Endurance Running Performance. Medicine and Science in Sports and Exercise , 47 (8), 1672–1681. https://doi.org/10.1249/MSS.0000000000000584
  • 4 Hooper, A. (n.d.). LibGuides: Systematic Review Resources: Systematic Reviews vs Other Types of Reviews . Retrieved February 7, 2020, from //libguides.sph.uth.tmc.edu/c.php?g=543382&p=5370369
  • 5 Rees, K., Takeda, A., Martin, N., Ellis, L., Wijesekara, D., Vepa, A., Das, A., Hartley, L., & Stranges, S. (2019). Mediterranean‐style diet for the primary and secondary prevention of cardiovascular disease. Cochrane Database of Systematic Reviews , 3 . doi.org/10.1002/14651858.CD009825.pub3
  • Figure 2.3. The hierarchy of evidence by Alice Callahan, is licensed under CC BY 4.0
  • Research lab photo by National Cancer Institute on Unsplas h ; mouse photo by vaun0815 on Unsplash
  • Figure 2.4. “Placebo effect example” by Lindshield, B. L. Kansas State University Human Nutrition (FNDH 400) Flexbook. goo.gl/vOAnR

Organizing Your Social Sciences Research Paper: Types of Research Designs

  • Purpose of Guide
  • Writing a Research Proposal
  • Design Flaws to Avoid
  • Independent and Dependent Variables
  • Narrowing a Topic Idea
  • Broadening a Topic Idea
  • The Research Problem/Question
  • Academic Writing Style
  • Choosing a Title
  • Making an Outline
  • Paragraph Development
  • The C.A.R.S. Model
  • Background Information
  • Theoretical Framework
  • Citation Tracking
  • Evaluating Sources
  • Reading Research Effectively
  • Primary Sources
  • Secondary Sources
  • What Is Scholarly vs. Popular?
  • Is it Peer-Reviewed?
  • Qualitative Methods
  • Quantitative Methods
  • Common Grammar Mistakes
  • Writing Concisely
  • Avoiding Plagiarism [linked guide]
  • Annotated Bibliography
  • Grading Someone Else's Paper

Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy that you choose to integrate the different components of the study in a coherent and logical way, thereby, ensuring you will effectively address the research problem; it constitutes the blueprint for the collection, measurement, and analysis of data. Note that your research problem determines the type of design you should use, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base . 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations far too early, before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing research designs in your paper can vary considerably, but any well-developed design will achieve the following :

  • Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
  • Review and synthesize previously published literature associated with the research problem,
  • Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
  • Effectively describe the data which will be necessary for an adequate testing of the hypotheses and explain how such data will be obtained, and
  • Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction and varies in length depending on the type of design you are using. However, you can get a sense of what to do by reviewing the literature of studies that have utilized the same research design. This can provide an outline to follow for your own paper.

NOTE : Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Video content

Videos in Business and Management , Criminology and Criminal Justice , Education , and Media, Communication and Cultural Studies specifically created for use in higher education.

A literature review tool that highlights the most influential works in Business & Management, Education, Politics & International Relations, Psychology and Sociology. Does not contain full text of the cited works. Dates vary.

Encyclopedias, handbooks, ebooks, and videos published by Sage and CQ Press. 2000 to present

Causal Design

Definition and Purpose

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

  • Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
  • Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
  • Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.

What do these studies tell you ?

  • Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
  • Replication is possible.
  • There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.

What these studies don't tell you ?

  • Not all relationships are casual! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
  • Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
  • If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the  actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation ; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base . 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, r ather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

  • Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
  • Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
  • The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
  • Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
  • Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
  • Either original data or secondary data can be used in this design.
  • In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
  • Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
  • Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101 . Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study . Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

  • Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
  • Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
  • Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
  • Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
  • Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
  • Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
  • Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
  • Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
  • Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
  • Studies cannot be utilized to establish cause and effect relationships.
  • This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
  • There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In  The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design, Application, Strengths and Weaknesses of Cross-Sectional Studies . Healthknowledge, 2009. Cross-Sectional Study . Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

  • The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
  • Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
  • If the limitations are understood, they can be a useful tool in developing a more focused study.
  • Descriptive studies can yield rich data that lead to important recommendations in practice.
  • Appoach collects a large amount of data for detailed analysis.
  • The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
  • Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
  • The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies . Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design , September 26, 2008. Explorable.com website.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

  • Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
  • Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
  • Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
  • Approach provides the highest level of evidence for single studies.
  • The design is artificial, and results may not generalize well to the real world.
  • The artificial settings of experiments may alter the behaviors or responses of participants.
  • Experimental designs can be costly if special equipment or facilities are needed.
  • Some research problems cannot be studied using an experiment because of ethical or technical reasons.
  • Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs . School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research . Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design . Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research . Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

  • Familiarity with basic details, settings, and concerns.
  • Well grounded picture of the situation being developed.
  • Generation of new ideas and assumptions.
  • Development of tentative theories or hypotheses.
  • Determination about whether a study is feasible in the future.
  • Issues get refined for more systematic investigation and formulation of new research questions.
  • Direction for future research and techniques get developed.
  • Design is a useful approach for gaining background information on a particular topic.
  • Exploratory research is flexible and can address research questions of all types (what, why, how).
  • Provides an opportunity to define new terms and clarify existing concepts.
  • Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
  • In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
  • Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
  • The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
  • The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
  • Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research . Wikipedia.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

  • The historical research design is unobtrusive; the act of research does not affect the results of the study.
  • The historical approach is well suited for trend analysis.
  • Historical records can add important contextual background required to more fully understand and interpret a research problem.
  • There is often no possibility of researcher-subject interaction that could affect the findings.
  • Historical sources can be used over and over to study different research problems or to replicate a previous study.
  • The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
  • Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
  • Interpreting historical sources can be very time consuming.
  • The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
  • Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
  • Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
  • It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58;  Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

  • Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
  • Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
  • The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
  • Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
  • The data collection method may change over time.
  • Maintaining the integrity of the original sample can be difficult over an extended period of time.
  • It can be difficult to show more than one variable at a time.
  • This design often needs qualitative research data to explain fluctuations in the results.
  • A longitudinal research design assumes present trends will continue unchanged.
  • It can take a long period of time to gather results.
  • There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study . Wikipedia.

Mixed-Method Design

  • Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
  • Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
  • A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
  • The strengths of one method can be used to overcome the inherent weaknesses of another method.
  • Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
  • May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
  • Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
  • A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
  • Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
  • Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
  • Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
  • Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
  • Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

  • Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
  • The researcher is able to collect in-depth information about a particular behavior.
  • Can reveal interrelationships among multifaceted dimensions of group interactions.
  • You can generalize your results to real life situations.
  • Observational research is useful for discovering what variables may be important before applying other methods like experiments.
  • Observation research designs account for the complexity of group behaviors.
  • Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
  • In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
  • There can be problems with bias as the researcher may only "see what they want to see."
  • There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
  • Sources or subjects may not all be equally credible.
  • Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research . Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

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1.6: Types of Statistical Studies (4 of 4)

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Learning Objectives

  • Based on the study design, determine what types of conclusions are appropriate.

Multitasking

Students sitting outdoors with laptop computers, cell phones, and mp3 players

Do you constantly text-message while in class? Do you jump from one website to another while doing homework? If so, then you are a high-tech multitasker. In a study of high-tech multitasking at Stanford University, researchers put 100 students into two groups: those who regularly do a lot of media multitasking and those who don’t. The two groups performed a series of three tasks:

(1) A task to measure the ability to pay attention:

  • Students view two images of red and blue rectangles flashed one after the other on a computer screen. They try to tell if the red rectangles are in a different position in the second frame.

(2) A task to measure control of memory:

  • Students view a sequence of letters flashed onto a computer screen, then recall which letters occurred more than once.

(3) A task to measure the ability to switch from one job to another:

  • Students view numbers and letters together with the instructions to pay attention to the numbers, then recall if the numbers were even or odd. Then the instructions switch. Students are to pay attention to the letters and recall if the letters were vowels or consonants.

On every task, the multitaskers did worse than the non-multitaskers.

The researchers concluded that “people who are regularly bombarded with several streams of electronic information do not pay attention, control their memory, or switch from one job to another as well as those who prefer to complete one task at a time” (as reported in Stanford News in 2009).

“When they’re [high-tech multitaskers] in situations where there are multiple sources of information coming from the external world or emerging out of memory, they’re not able to filter out what’s not relevant to their current goal,” said Wagner, an associate professor of psychology at Stanford. “That failure to filter means they’re slowed down by that irrelevant information.”

https://assessments.lumenlearning.co...sessments/3403

In general, we should not make cause-and-effect statements from observational studies, but in reality, researchers do it all the time. This does not mean that researchers are drawing incorrect conclusions from observational studies. Instead, they have developed techniques that go a long way toward decreasing the impact of confounding variables. These techniques are beyond the scope of this course, but we briefly discuss a simplified example to illustrate the idea.

Smoking and Cancer

Lit cigarette in ashtray

Consider this excerpt from the National Cancer Institute website:

  • Smoking is a leading cause of cancer and of death from cancer. Millions of Americans have health problems caused by smoking. Cigarette smoking and exposure to tobacco smoke cause an estimated average of 438,000 premature deaths each year in the United States.

Notice that the National Cancer Institute clearly states a cause-and-effect relationship between smoking and cancer. Now let’s think about the evidence that is required to establish this causal link. Researchers would need to conduct experiments similar to the hormone replacement therapy experiments done by the Women’s Health Initiative. Such experiments would be very difficult to do. The researchers cannot manipulate the smoking variable. Doing so would require them to randomly assign people to smoke or to abstain from smoking their whole life. Obviously, this is impossible. So how can we say that smoking causes cancer?

In practice, researchers approach this challenge in a variety of ways. They may use advanced techniques for making statistical adjustments within an observational study to control the effects of confounding variables that could influence the results. A simple example is the cell phone and brain cancer study.

  • In this observational study, researchers identified a group of 469 people with brain cancer. They paired each person who had brain cancer with a person of the same sex, of similar age, and of the same race who did not have brain cancer. Then they compared the cell phone use for each pair of people. This matching attempts to control the confounding effects of sex, age, and race on the response variable, cancer. With these adjustments, the study will provide stronger evidence for (or against) a casual link.

However, even with such adjustments, we should be cautious about using evidence from an observational study to establish a cause-and-effect relationship. Researchers used these types of adjustments in the observational studies with hormone replacement therapy. We saw in that research that the results were still misleading when compared to those of an experiment.

So how can the National Cancer Institute state as a fact that smoking causes cancer?

They used other nonstatistical guidelines to build evidence for a cause-and-effect relationship from observational studies. In this approach, researchers review a large number of observational studies with criteria that, if met, provide stronger evidence of a possible cause-and-effect relationship. Here are some simplified examples of the criteria they use:

(1) There is a reasonable explanation for how one variable might cause the other.

  • For example, experiments with rats show that chemicals found in cigarettes cause cancer in rats. It is therefor reasonable to infer that these same chemicals may cause cancer in humans.
  • Consider these experiments together with the observational studies showing the association between smoking and cancer in humans. We now have more convincing evidence of a possible cause-and-effect relationship between smoking and cancer in humans.

(2) The observational studies vary in design so that factors that confound one study are not present in another.

  • For example, one observational study shows an association between smoking and lung cancer, but the people in the study all live in a large city. Air pollution in a large city may contribute to the lung cancer, so we cannot be sure that smoking is the cause of cancer in this study.
  • Another observational study looks only at nonsmokers. This study shows no difference in lung cancer rates for nonsmokers living in rural areas compared to nonsmokers living in cities.
  • Consider these two studies together. The second study suggests that air pollution does not contribute to lung cancer, so we now have more convincing evidence that smoking (not air pollution) is the cause of higher cancer rates in the first study.

Let’s Summarize

  • Ask a question that can be answered by collecting data.
  • Decide what to measure, and then collect data.
  • Summarize and analyze.
  • Draw a conclusion, and communicate the results.
  • Questions about a population
  • Questions about cause-and-effect
  • To answer a question about a population, we select a sample and conduct an observational study. To answer a question about cause-and-effect we conduct an experiment.
  • Observational studies: An observational study observes individuals and measures variables of interest. We conduct observational studies to investigate questions about a population or about an association between two variables. An observational study alone does not provide convincing evidence of a cause-and-effect relationship.
  • Experiments: An experiment intentionally manipulates one variable in an attempt to cause an effect on another variable. The primary goal of an experiment is to provide evidence for a cause-and-effect relationship between two variables.
  • In statistics, a variable is information we gather about individuals or objects.
  • When we investigate a relationship between two variables, we identify an explanatory variable and a response variable. To establish a cause-and-effect relationship, we want to make sure the explanatory variable is the only thing that impacts the response variable. Other factors, however, may also influence the response. These other factors are called confounding variables.
  • The influence of confounding variables on the response variable is one of the reasons that an observational study gives weak, and potentially misleading, evidence of a cause-and-effect relationship. A well-designed experiment takes steps to eliminate the effects of confounding variables, such as random assignment of people to treatment groups, use of a placebo, and blind conditions. For this reason, a well-designed experiment provides convincing evidence of cause-and-effect.

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TYPES OF RESEARCH.

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TYPES OF RESEARCH

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Types of research designs

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High-quality research designs must be selected by the researchers so that, the researchers can use those designs to convey those ideas readers in an understandable mode. Both of these tasks are difficult for researchers. The significant features of the research design are framed in an evidently complete form which can be used in qualitative, quantitative, and combination of methods for research. Specific features like inquiry (I), a model of the world (M), a data strategy (D), and a strategy for an answer (A) must be defined in the design approach. These features which provide adequate information needed for researchers and readers must be introduced in the code so as to understand the techniques to analyze bias, accuracy, and power of qualitative inferences. The selection of articles was based on the criterion of inclusion-exclusion that is approved by all authors. The dataset consists of (1) Articles and reviews (2) Studies related to the medical sector (3) Studies in the English language (4) Studies related to big data analytics. Click the link to read: https://bit.ly/2ZWZPM1 Contact: Website: www.tutorsindia.com Email: [email protected] United Kingdom: +44-1143520021 India: +91-4448137070 Whatsapp Number: +91-8754446690

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Research Method Vs Research Design Students are usually confused about research methods and research designs. These may appear the same, but they are different. Research Methods Research methods can be conceived as various processes, procedures, and tools employed to collect and analyze research data. They are approaches used to execute research plans. A research method is a research paradigm or philosophical framework that research is based. There are three commonest methods in research namely, quantitative, qualitative, and mixed methods. These methods are an umbrella for various research designs. Research Designs Research designs are the overall research structure of a study which help to ensure that the data collected effectively answers the research question(s). Research designs can be Descriptive (e.g., case-study, naturalistic observation, survey), Correlational (e.g., case-control study, observational study), Experimental (e.g., field experiment, controlled experiment, quasiexperiment), Review (literature review, systematic review), and Meta-analytic (meta-analysis) in nature. They can, however, be grouped under research methods. Note that the nature of the research will determine the research method as well as the appropriate research design.

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Types of Research

Apr 01, 2019

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Types of Research. The types of research are determined by the aims of the researcher. A. Based on the Researcher’s Objective. Pure Research when the research is conducted solely to come up with new knowledge or to have a fuller understanding of a particular subject. Applied Research

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Types of Research The types of research are determined by the aims of the researcher.

A. Based on the Researcher’s Objective Pure Research when the research is conducted solely to come up with new knowledge or to have a fuller understanding of a particular subject. Applied Research If the research is done to find an application of the knowledge whether new or old.

B. Based on the conditions under which the study is conducted Descriptive Research type of research that observes and records the changes as they happen in nature. The changes are can’t be manipulated. Experimental Research In its simplest form, experimental research involves comparing two groups on one outcome measure to test some hypothesis regarding causation.

Finding a Topic • People, places and objects around you are possible sources of your research. • The communities where you live are also rich sources of research topics. • It would also be helpful to talk to scientists, researchers or teachers by visiting them in their places of work.

List of Topics • Alternative or nonconventional sources of energy. • Botanical pesticides • Control of environmental pollution • Product development • Food processing • Herbal medicine (antimicrobial property) • Computer science • Biodegradable plastic

Getting Essential Informationfrom Print Sources • Make a list of 5 possible places where you might find as much information about your topic as you can. Public Library Local College or University Public Hospitals Pharmaceutical Companies Research Institution (RITM)

Understanding Printed Sources • Printed material generally includes books, newspapers, magazines, pamphlets, or excerpts of essays—in other words, any written material on your topic. These printed materials are usually grouped into two categories: primary & secondary sources

Primary Sources • All primary source materials are firsthand accounts of circumstances by individuals who are directly involved or have experienced what they are writing about firsthand.

Secondary Sources • Books • magazine articles • pamphlets by authors

Getting Essential Informationfrom Online Sources • Rather than having to go to a library or other institution to seek out and investigate your sources, the Internet brings them to you. You should know that some Internet sites and search engines are better than others.

Citing ReferenceSources? “Borrowed thoughts, like borrowed money, only show the poverty of the borrower.” MARGUERITE CJARDINER

What is Plagiarism? Plagiarism is the technical term for using someone else's words without giving adequate credit.

When and How to Cite the Reference Sources • When you write your research paper you might want to copy words, pictures, diagrams, or ideas from one of your sources. It is OK to copy such information as long as you reference it with a citation.

When and How to Cite the Reference Sources • For a science fair project, a reference citation (a.k.a. author-date citation) is an accepted way to reference information you copy.

How to Cite the Reference Sources • Make sure that the source for every citation item copied appears in your bibliography. • Simply put the author's last name, the year of publication, and page number (if needed) in parentheses after the information you copy. • Place the reference citation at the end of the sentence but before the final period.

Examples of Reference Citations using APA Format (American Psychological Association) • "If you copy a sentence from a book or magazine article by a single author, the reference will look like this. A comma separates the page number (or numbers) from the year" (Bloggs, 2002, p. 37).

Examples of Reference Citations using APA Format • "If you copy a sentence from a book or magazine article by more than one author, the reference will look like this" (Bloggs & Smith, 2002, p. 37).

Examples of Reference Citations using APA Format • "Sometimes the author will have two publications in your bibliography for just one year. In that case, the first publication would have an 'a' after the publication year, the second a 'b', and so on. The reference will look like this" (Nguyen, 2000b).

Examples of Reference Citations using APA Format • "When the author is unknown, the text reference for such an entry may substitute the title, or a shortened version of the title for the author" (The Chicago Manual, 1993). • "For reference citations, only direct quotes need page numbers" (Han, 1995).

Remember… • Plagiarismis when someone copies the words, pictures, diagrams, or ideas of someone else and presents them as his or her own. • When you find information in a book, on the Internet, or from some other source, you MUST give the author of that information credit in a citation. • If you copy a sentence or paragraph exactly, you should also use quotation marks around the text.

Writing a Bibliography: APA Format • Your list of works cited should begin at the end of the paper on a new page with the centered title, References. • Alphabetize the entries in your list by the author's last name, using the letter-by-letter system (ignore spaces and other punctuation.) • Only the initials of the first and middle names are given. • If the author's name is unknown, alphabetize by the title, ignoring any A, An, or The.

Writing a Bibliography: APA Format Underlining or Italics? • When reports were written on typewriters, the names of publications were underlined because most typewriters had no way to print italics. • If you write a bibliography by hand, you should still underline the names of publications. But, if you use a computer, then publication names should be in italics as they are below.

Writing a Bibliography: APA Format • If there is more than one author, use an ampersand (&) before the name of the last author. • If there are more than six authors, list only the first one and use et al. for the rest. • Place the date of publication in parentheses immediately after the name of the author. • Place a period after the closing parenthesis.

Writing a Bibliography: APA Format Books Format:Author's last name, first initial. (Publication date). Book title. Additional information. City of publication: Publishing company. Examples: • Allen, T. (1974). Vanishing wildlife of North America. Washington, D.C.: National Geographic Society. • Boorstin, D. (1992). The creators: A history of the heroes of the imagination. New York: Random House. • Searles, B., & Last, M. (1979). A reader's guide to science fiction. New York: Facts on File, Inc.

Writing a Bibliography: APA Format Encyclopedia & Dictionary Format:Author's last name, first initial. (Date). Title of Article. Title of Encyclopedia (Volume, pages). City of publication: Publishing company. Examples: • Bergmann, P. G. (1993). Relativity. In The new encyclopedia britannica (Vol. 26, pp. 501-508). Chicago: Encyclopedia Britannica. • Merriam-Webster's collegiate dictionary (10th ed.). (1993). Springfield, MA: Merriam-Webster. • Pettingill, O. S., Jr. (1980). Falcon and Falconry. World book encyclopedia. (pp. 150-155). Chicago: World Book.

Writing a Bibliography: APA Format Magazine & Newspaper Articles Format:Author's last name, first initial. (Publication date). Article title. Periodical title, volume number(issue number if available), inclusive pages. Note: Do not enclose the title in quotation marks. Put a period after the title. If a periodical includes a volume number, italicize it and then give the page range (in regular type) without "pp." If the periodical does not use volume numbers, as in newspapers, use p. or pp. for page numbers. Note: Unlike other periodicals, p. or pp. precedes page numbers for a newspaper reference in APA style.

Writing a Bibliography: APA Format Magazine & Newspaper Examples: • Harlow, H. F. (1983). Fundamentals for preparing psychology journal articles. Journal of Comparative and Physiological Psychology, 55, 893-896. • Henry, W. A., III. (1990, April 9). Making the grade in today's schools. Time, 135, 28-31. • Kalette, D. (1986, July 21). California town counts town to big quake. USA Today, 9, p. A1. • Kanfer, S. (1986, July 21). Heard any good books lately? Time, 113, 71-72.

Writing a Bibliography: APA Format Website or Webpage Format:Online periodical:Author's name. (Date of publication). Title of article. Title of Periodical, volume number, Retrieved month day, year, from full URL Online document:Author's name. (Date of publication). Title of work. Retrieved month day, year, from full URL Note: When citing Internet sources, refer to the specific website document. If a document is undated, use "n.d." (for no date) immediately after the document title. Break a lengthy URL that goes to another line after a slash or before a period. Continually check your references to online documents. There is no period following a URL. Note: If you cannot find some of this information, cite what is available.

Writing a Bibliography: APA Format Examples of Website or web page references: • Devitt, T. (2001, August 2). Lightning injures four at music festival. The Why? Files. Retrieved January 23, 2002, from http://whyfiles.org/137lightning/index.html • Dove, R. (1998). Lady freedom among us. The Electronic Text Center. Retrieved June 19, 1998, from Alderman Library, University of Virginia website: http://etext.lib.virginia.edu/subjects/afam.html

Reference citation using M.l.a. format (Modern language association)

MLA Documentation

Writing a Bibliography: MLA Format • Your list of works cited should begin at the end of the paper on a new page with the centered title, Works Cited. • Alphabetize the entries in your list by the author's last name, using the letter-by-letter system (ignore spaces and other punctuation.) • If the author's name is unknown, alphabetize by the title, ignoring any A, An, or The.

Capitalization, Abbreviation, and Punctuation • The MLA guidelines specify using title case capitalization - capitalize the first words, the last words, and all principal words, including those that follow hyphens in compound terms. Use lowercase abbreviations to identify the parts of a work (e.g., vol. for volume, ed. for editor) except when these designations follow a period.

Capitalization, Abbreviation, and Punctuation • Separate author, title, and publication information with a period followed by one space. Use a colon and a space to separate a title from a subtitle. Include other kinds of punctuation only if it is part of the title. Use quotation marks to indicate the titles of short works appearing within larger works (e.g., "Memories of Childhood." American Short Stories). Also use quotation marks for titles of unpublished works and songs.

Writing a Bibliography: MLA Format Underlining or Italics? • When reports were written on typewriters, the names of publications were underlined because most typewriters had no way to print italics. If you write a bibliography by hand, you should still underline the names of publications. But, if you use a computer, then publication names should be in italics as they are below.

MLA Format Samples Books Format:Author's last name, first name. Book title. Additional information. City of publication: Publishing company, publication date. Examples: • De Vera, Jaime S. Vanishing Wildlife of North America. Washington, D.C.: National Geographic Society, 1974. • Boorstin, Daniel J. The Creators: A History of the Heroes of the Imagination. New York: Random, 1992.

MLA Format Samples Encyclopedia & Dictionary Format:Author's last name, first name. "Title of Article." Title of Encyclopedia. Date. Note: If the dictionary or encyclopedia arranges articles alphabetically, you may omit volume and page numbers. Examples: • Pettingill, Olin Sewall, Jr. "Falcon and Falconry." World Book Encyclopedia. 1980. • Tobias, Richard. "Thurber, James." Encyclopedia Americana. 1991 ed.

MLA Format Samples Magazine & Newspaper Articles Format:Author's last name, first name. "Article title." Periodical title Volume # Date: inclusive pages. Examples: • Kanfer, Stefan. "Heard Any Good Books Lately?" Time 113 21 July 1986: 71-72. • Trillin, Calvin. "Culture Shopping." New Yorker 15 Feb. 1993: 48-51.

MLA Format Samples Website or Webpage Format:Author's last name, first name (if available). "Title of work within a project or database." Title of site, project, or database. Editor (if available). Electronic publication information (Date of publication or of the latest update, and name of any sponsoring institution or organization). Date of access and <full URL>. Note: If you cannot find some of this information, cite what is available. Examples: • Devitt, Terry. "Lightning injures four at music festival." The Why? Files. 2 Aug. 2001. 23 Jan. 2002 <http://whyfiles.org/137lightning/index.html>. • Dove, Rita. "Lady Freedom among Us." The Electronic Text Center. Ed. David Seaman. 1998. Alderman Lib., U of Virginia. 19 June 1998 <http://etext.lib.virginia.edu/subjects/afam.html>.

THINGS TO CONSIDER IN DOING S.I.P.

Plan Your Project Success Calendar Planned Date Date Completed 1. Choosing a topic (2-5 days) ____________ ______________ 2. Collecting background information ____________ ______________ (1-3 weeks) 3. Problem and hypothesis (1-4 days) ____________ ______________ 4. Design for experiment (1 week) ____________ ______________ 5. Getting materials ready for ____________ ______________ experiment (1 week) 6. Making the data table (1-2 weeks) ____________ ______________ 7. Recording in the data table ____________ ______________ (1-2 weeks) 8. Stating results (1 week) ____________ _____________ 9. Drawing conclusions (1 week) ____________ ______________ 10. Compiling a bibliography (2-3days) ____________ ______________ 11. Making the display (1-2 weeks) ____________ ______________

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  • Systematic review
  • Open access
  • Published: 19 February 2024

‘It depends’: what 86 systematic reviews tell us about what strategies to use to support the use of research in clinical practice

  • Annette Boaz   ORCID: orcid.org/0000-0003-0557-1294 1 ,
  • Juan Baeza 2 ,
  • Alec Fraser   ORCID: orcid.org/0000-0003-1121-1551 2 &
  • Erik Persson 3  

Implementation Science volume  19 , Article number:  15 ( 2024 ) Cite this article

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The gap between research findings and clinical practice is well documented and a range of strategies have been developed to support the implementation of research into clinical practice. The objective of this study was to update and extend two previous reviews of systematic reviews of strategies designed to implement research evidence into clinical practice.

We developed a comprehensive systematic literature search strategy based on the terms used in the previous reviews to identify studies that looked explicitly at interventions designed to turn research evidence into practice. The search was performed in June 2022 in four electronic databases: Medline, Embase, Cochrane and Epistemonikos. We searched from January 2010 up to June 2022 and applied no language restrictions. Two independent reviewers appraised the quality of included studies using a quality assessment checklist. To reduce the risk of bias, papers were excluded following discussion between all members of the team. Data were synthesised using descriptive and narrative techniques to identify themes and patterns linked to intervention strategies, targeted behaviours, study settings and study outcomes.

We identified 32 reviews conducted between 2010 and 2022. The reviews are mainly of multi-faceted interventions ( n  = 20) although there are reviews focusing on single strategies (ICT, educational, reminders, local opinion leaders, audit and feedback, social media and toolkits). The majority of reviews report strategies achieving small impacts (normally on processes of care). There is much less evidence that these strategies have shifted patient outcomes. Furthermore, a lot of nuance lies behind these headline findings, and this is increasingly commented upon in the reviews themselves.

Combined with the two previous reviews, 86 systematic reviews of strategies to increase the implementation of research into clinical practice have been identified. We need to shift the emphasis away from isolating individual and multi-faceted interventions to better understanding and building more situated, relational and organisational capability to support the use of research in clinical practice. This will involve drawing on a wider range of research perspectives (including social science) in primary studies and diversifying the types of synthesis undertaken to include approaches such as realist synthesis which facilitate exploration of the context in which strategies are employed.

Peer Review reports

Contribution to the literature

Considerable time and money is invested in implementing and evaluating strategies to increase the implementation of research into clinical practice.

The growing body of evidence is not providing the anticipated clear lessons to support improved implementation.

Instead what is needed is better understanding and building more situated, relational and organisational capability to support the use of research in clinical practice.

This would involve a more central role in implementation science for a wider range of perspectives, especially from the social, economic, political and behavioural sciences and for greater use of different types of synthesis, such as realist synthesis.

Introduction

The gap between research findings and clinical practice is well documented and a range of interventions has been developed to increase the implementation of research into clinical practice [ 1 , 2 ]. In recent years researchers have worked to improve the consistency in the ways in which these interventions (often called strategies) are described to support their evaluation. One notable development has been the emergence of Implementation Science as a field focusing explicitly on “the scientific study of methods to promote the systematic uptake of research findings and other evidence-based practices into routine practice” ([ 3 ] p. 1). The work of implementation science focuses on closing, or at least narrowing, the gap between research and practice. One contribution has been to map existing interventions, identifying 73 discreet strategies to support research implementation [ 4 ] which have been grouped into 9 clusters [ 5 ]. The authors note that they have not considered the evidence of effectiveness of the individual strategies and that a next step is to understand better which strategies perform best in which combinations and for what purposes [ 4 ]. Other authors have noted that there is also scope to learn more from other related fields of study such as policy implementation [ 6 ] and to draw on methods designed to support the evaluation of complex interventions [ 7 ].

The increase in activity designed to support the implementation of research into practice and improvements in reporting provided the impetus for an update of a review of systematic reviews of the effectiveness of interventions designed to support the use of research in clinical practice [ 8 ] which was itself an update of the review conducted by Grimshaw and colleagues in 2001. The 2001 review [ 9 ] identified 41 reviews considering a range of strategies including educational interventions, audit and feedback, computerised decision support to financial incentives and combined interventions. The authors concluded that all the interventions had the potential to promote the uptake of evidence in practice, although no one intervention seemed to be more effective than the others in all settings. They concluded that combined interventions were more likely to be effective than single interventions. The 2011 review identified a further 13 systematic reviews containing 313 discrete primary studies. Consistent with the previous review, four main strategy types were identified: audit and feedback; computerised decision support; opinion leaders; and multi-faceted interventions (MFIs). Nine of the reviews reported on MFIs. The review highlighted the small effects of single interventions such as audit and feedback, computerised decision support and opinion leaders. MFIs claimed an improvement in effectiveness over single interventions, although effect sizes remained small to moderate and this improvement in effectiveness relating to MFIs has been questioned in a subsequent review [ 10 ]. In updating the review, we anticipated a larger pool of reviews and an opportunity to consolidate learning from more recent systematic reviews of interventions.

This review updates and extends our previous review of systematic reviews of interventions designed to implement research evidence into clinical practice. To identify potentially relevant peer-reviewed research papers, we developed a comprehensive systematic literature search strategy based on the terms used in the Grimshaw et al. [ 9 ] and Boaz, Baeza and Fraser [ 8 ] overview articles. To ensure optimal retrieval, our search strategy was refined with support from an expert university librarian, considering the ongoing improvements in the development of search filters for systematic reviews since our first review [ 11 ]. We also wanted to include technology-related terms (e.g. apps, algorithms, machine learning, artificial intelligence) to find studies that explored interventions based on the use of technological innovations as mechanistic tools for increasing the use of evidence into practice (see Additional file 1 : Appendix A for full search strategy).

The search was performed in June 2022 in the following electronic databases: Medline, Embase, Cochrane and Epistemonikos. We searched for articles published since the 2011 review. We searched from January 2010 up to June 2022 and applied no language restrictions. Reference lists of relevant papers were also examined.

We uploaded the results using EPPI-Reviewer, a web-based tool that facilitated semi-automation of the screening process and removal of duplicate studies. We made particular use of a priority screening function to reduce screening workload and avoid ‘data deluge’ [ 12 ]. Through machine learning, one reviewer screened a smaller number of records ( n  = 1200) to train the software to predict whether a given record was more likely to be relevant or irrelevant, thus pulling the relevant studies towards the beginning of the screening process. This automation did not replace manual work but helped the reviewer to identify eligible studies more quickly. During the selection process, we included studies that looked explicitly at interventions designed to turn research evidence into practice. Studies were included if they met the following pre-determined inclusion criteria:

The study was a systematic review

Search terms were included

Focused on the implementation of research evidence into practice

The methodological quality of the included studies was assessed as part of the review

Study populations included healthcare providers and patients. The EPOC taxonomy [ 13 ] was used to categorise the strategies. The EPOC taxonomy has four domains: delivery arrangements, financial arrangements, governance arrangements and implementation strategies. The implementation strategies domain includes 20 strategies targeted at healthcare workers. Numerous EPOC strategies were assessed in the review including educational strategies, local opinion leaders, reminders, ICT-focused approaches and audit and feedback. Some strategies that did not fit easily within the EPOC categories were also included. These were social media strategies and toolkits, and multi-faceted interventions (MFIs) (see Table  2 ). Some systematic reviews included comparisons of different interventions while other reviews compared one type of intervention against a control group. Outcomes related to improvements in health care processes or patient well-being. Numerous individual study types (RCT, CCT, BA, ITS) were included within the systematic reviews.

We excluded papers that:

Focused on changing patient rather than provider behaviour

Had no demonstrable outcomes

Made unclear or no reference to research evidence

The last of these criteria was sometimes difficult to judge, and there was considerable discussion amongst the research team as to whether the link between research evidence and practice was sufficiently explicit in the interventions analysed. As we discussed in the previous review [ 8 ] in the field of healthcare, the principle of evidence-based practice is widely acknowledged and tools to change behaviour such as guidelines are often seen to be an implicit codification of evidence, despite the fact that this is not always the case.

Reviewers employed a two-stage process to select papers for inclusion. First, all titles and abstracts were screened by one reviewer to determine whether the study met the inclusion criteria. Two papers [ 14 , 15 ] were identified that fell just before the 2010 cut-off. As they were not identified in the searches for the first review [ 8 ] they were included and progressed to assessment. Each paper was rated as include, exclude or maybe. The full texts of 111 relevant papers were assessed independently by at least two authors. To reduce the risk of bias, papers were excluded following discussion between all members of the team. 32 papers met the inclusion criteria and proceeded to data extraction. The study selection procedure is documented in a PRISMA literature flow diagram (see Fig.  1 ). We were able to include French, Spanish and Portuguese papers in the selection reflecting the language skills in the study team, but none of the papers identified met the inclusion criteria. Other non- English language papers were excluded.

figure 1

PRISMA flow diagram. Source: authors

One reviewer extracted data on strategy type, number of included studies, local, target population, effectiveness and scope of impact from the included studies. Two reviewers then independently read each paper and noted key findings and broad themes of interest which were then discussed amongst the wider authorial team. Two independent reviewers appraised the quality of included studies using a Quality Assessment Checklist based on Oxman and Guyatt [ 16 ] and Francke et al. [ 17 ]. Each study was rated a quality score ranging from 1 (extensive flaws) to 7 (minimal flaws) (see Additional file 2 : Appendix B). All disagreements were resolved through discussion. Studies were not excluded in this updated overview based on methodological quality as we aimed to reflect the full extent of current research into this topic.

The extracted data were synthesised using descriptive and narrative techniques to identify themes and patterns in the data linked to intervention strategies, targeted behaviours, study settings and study outcomes.

Thirty-two studies were included in the systematic review. Table 1. provides a detailed overview of the included systematic reviews comprising reference, strategy type, quality score, number of included studies, local, target population, effectiveness and scope of impact (see Table  1. at the end of the manuscript). Overall, the quality of the studies was high. Twenty-three studies scored 7, six studies scored 6, one study scored 5, one study scored 4 and one study scored 3. The primary focus of the review was on reviews of effectiveness studies, but a small number of reviews did include data from a wider range of methods including qualitative studies which added to the analysis in the papers [ 18 , 19 , 20 , 21 ]. The majority of reviews report strategies achieving small impacts (normally on processes of care). There is much less evidence that these strategies have shifted patient outcomes. In this section, we discuss the different EPOC-defined implementation strategies in turn. Interestingly, we found only two ‘new’ approaches in this review that did not fit into the existing EPOC approaches. These are a review focused on the use of social media and a review considering toolkits. In addition to single interventions, we also discuss multi-faceted interventions. These were the most common intervention approach overall. A summary is provided in Table  2 .

Educational strategies

The overview identified three systematic reviews focusing on educational strategies. Grudniewicz et al. [ 22 ] explored the effectiveness of printed educational materials on primary care physician knowledge, behaviour and patient outcomes and concluded they were not effective in any of these aspects. Koota, Kääriäinen and Melender [ 23 ] focused on educational interventions promoting evidence-based practice among emergency room/accident and emergency nurses and found that interventions involving face-to-face contact led to significant or highly significant effects on patient benefits and emergency nurses’ knowledge, skills and behaviour. Interventions using written self-directed learning materials also led to significant improvements in nurses’ knowledge of evidence-based practice. Although the quality of the studies was high, the review primarily included small studies with low response rates, and many of them relied on self-assessed outcomes; consequently, the strength of the evidence for these outcomes is modest. Wu et al. [ 20 ] questioned if educational interventions aimed at nurses to support the implementation of evidence-based practice improve patient outcomes. Although based on evaluation projects and qualitative data, their results also suggest that positive changes on patient outcomes can be made following the implementation of specific evidence-based approaches (or projects). The differing positive outcomes for educational strategies aimed at nurses might indicate that the target audience is important.

Local opinion leaders

Flodgren et al. [ 24 ] was the only systemic review focusing solely on opinion leaders. The review found that local opinion leaders alone, or in combination with other interventions, can be effective in promoting evidence‐based practice, but this varies both within and between studies and the effect on patient outcomes is uncertain. The review found that, overall, any intervention involving opinion leaders probably improves healthcare professionals’ compliance with evidence-based practice but varies within and across studies. However, how opinion leaders had an impact could not be determined because of insufficient details were provided, illustrating that reporting specific details in published studies is important if diffusion of effective methods of increasing evidence-based practice is to be spread across a system. The usefulness of this review is questionable because it cannot provide evidence of what is an effective opinion leader, whether teams of opinion leaders or a single opinion leader are most effective, or the most effective methods used by opinion leaders.

Pantoja et al. [ 26 ] was the only systemic review focusing solely on manually generated reminders delivered on paper included in the overview. The review explored how these affected professional practice and patient outcomes. The review concluded that manually generated reminders delivered on paper as a single intervention probably led to small to moderate increases in adherence to clinical recommendations, and they could be used as a single quality improvement intervention. However, the authors indicated that this intervention would make little or no difference to patient outcomes. The authors state that such a low-tech intervention may be useful in low- and middle-income countries where paper records are more likely to be the norm.

ICT-focused approaches

The three ICT-focused reviews [ 14 , 27 , 28 ] showed mixed results. Jamal, McKenzie and Clark [ 14 ] explored the impact of health information technology on the quality of medical and health care. They examined the impact of electronic health record, computerised provider order-entry, or decision support system. This showed a positive improvement in adherence to evidence-based guidelines but not to patient outcomes. The number of studies included in the review was low and so a conclusive recommendation could not be reached based on this review. Similarly, Brown et al. [ 28 ] found that technology-enabled knowledge translation interventions may improve knowledge of health professionals, but all eight studies raised concerns of bias. The De Angelis et al. [ 27 ] review was more promising, reporting that ICT can be a good way of disseminating clinical practice guidelines but conclude that it is unclear which type of ICT method is the most effective.

Audit and feedback

Sykes, McAnuff and Kolehmainen [ 29 ] examined whether audit and feedback were effective in dementia care and concluded that it remains unclear which ingredients of audit and feedback are successful as the reviewed papers illustrated large variations in the effectiveness of interventions using audit and feedback.

Non-EPOC listed strategies: social media, toolkits

There were two new (non-EPOC listed) intervention types identified in this review compared to the 2011 review — fewer than anticipated. We categorised a third — ‘care bundles’ [ 36 ] as a multi-faceted intervention due to its description in practice and a fourth — ‘Technology Enhanced Knowledge Transfer’ [ 28 ] was classified as an ICT-focused approach. The first new strategy was identified in Bhatt et al.’s [ 30 ] systematic review of the use of social media for the dissemination of clinical practice guidelines. They reported that the use of social media resulted in a significant improvement in knowledge and compliance with evidence-based guidelines compared with more traditional methods. They noted that a wide selection of different healthcare professionals and patients engaged with this type of social media and its global reach may be significant for low- and middle-income countries. This review was also noteworthy for developing a simple stepwise method for using social media for the dissemination of clinical practice guidelines. However, it is debatable whether social media can be classified as an intervention or just a different way of delivering an intervention. For example, the review discussed involving opinion leaders and patient advocates through social media. However, this was a small review that included only five studies, so further research in this new area is needed. Yamada et al. [ 31 ] draw on 39 studies to explore the application of toolkits, 18 of which had toolkits embedded within larger KT interventions, and 21 of which evaluated toolkits as standalone interventions. The individual component strategies of the toolkits were highly variable though the authors suggest that they align most closely with educational strategies. The authors conclude that toolkits as either standalone strategies or as part of MFIs hold some promise for facilitating evidence use in practice but caution that the quality of many of the primary studies included is considered weak limiting these findings.

Multi-faceted interventions

The majority of the systematic reviews ( n  = 20) reported on more than one intervention type. Some of these systematic reviews focus exclusively on multi-faceted interventions, whilst others compare different single or combined interventions aimed at achieving similar outcomes in particular settings. While these two approaches are often described in a similar way, they are actually quite distinct from each other as the former report how multiple strategies may be strategically combined in pursuance of an agreed goal, whilst the latter report how different strategies may be incidentally used in sometimes contrasting settings in the pursuance of similar goals. Ariyo et al. [ 35 ] helpfully summarise five key elements often found in effective MFI strategies in LMICs — but which may also be transferrable to HICs. First, effective MFIs encourage a multi-disciplinary approach acknowledging the roles played by different professional groups to collectively incorporate evidence-informed practice. Second, they utilise leadership drawing on a wide set of clinical and non-clinical actors including managers and even government officials. Third, multiple types of educational practices are utilised — including input from patients as stakeholders in some cases. Fourth, protocols, checklists and bundles are used — most effectively when local ownership is encouraged. Finally, most MFIs included an emphasis on monitoring and evaluation [ 35 ]. In contrast, other studies offer little information about the nature of the different MFI components of included studies which makes it difficult to extrapolate much learning from them in relation to why or how MFIs might affect practice (e.g. [ 28 , 38 ]). Ultimately, context matters, which some review authors argue makes it difficult to say with real certainty whether single or MFI strategies are superior (e.g. [ 21 , 27 ]). Taking all the systematic reviews together we may conclude that MFIs appear to be more likely to generate positive results than single interventions (e.g. [ 34 , 45 ]) though other reviews should make us cautious (e.g. [ 32 , 43 ]).

While multi-faceted interventions still seem to be more effective than single-strategy interventions, there were important distinctions between how the results of reviews of MFIs are interpreted in this review as compared to the previous reviews [ 8 , 9 ], reflecting greater nuance and debate in the literature. This was particularly noticeable where the effectiveness of MFIs was compared to single strategies, reflecting developments widely discussed in previous studies [ 10 ]. We found that most systematic reviews are bounded by their clinical, professional, spatial, system, or setting criteria and often seek to draw out implications for the implementation of evidence in their areas of specific interest (such as nursing or acute care). Frequently this means combining all relevant studies to explore the respective foci of each systematic review. Therefore, most reviews we categorised as MFIs actually include highly variable numbers and combinations of intervention strategies and highly heterogeneous original study designs. This makes statistical analyses of the type used by Squires et al. [ 10 ] on the three reviews in their paper not possible. Further, it also makes extrapolating findings and commenting on broad themes complex and difficult. This may suggest that future research should shift its focus from merely examining ‘what works’ to ‘what works where and what works for whom’ — perhaps pointing to the value of realist approaches to these complex review topics [ 48 , 49 ] and other more theory-informed approaches [ 50 ].

Some reviews have a relatively small number of studies (i.e. fewer than 10) and the authors are often understandably reluctant to engage with wider debates about the implications of their findings. Other larger studies do engage in deeper discussions about internal comparisons of findings across included studies and also contextualise these in wider debates. Some of the most informative studies (e.g. [ 35 , 40 ]) move beyond EPOC categories and contextualise MFIs within wider systems thinking and implementation theory. This distinction between MFIs and single interventions can actually be very useful as it offers lessons about the contexts in which individual interventions might have bounded effectiveness (i.e. educational interventions for individual change). Taken as a whole, this may also then help in terms of how and when to conjoin single interventions into effective MFIs.

In the two previous reviews, a consistent finding was that MFIs were more effective than single interventions [ 8 , 9 ]. However, like Squires et al. [ 10 ] this overview is more equivocal on this important issue. There are four points which may help account for the differences in findings in this regard. Firstly, the diversity of the systematic reviews in terms of clinical topic or setting is an important factor. Secondly, there is heterogeneity of the studies within the included systematic reviews themselves. Thirdly, there is a lack of consistency with regards to the definition and strategies included within of MFIs. Finally, there are epistemological differences across the papers and the reviews. This means that the results that are presented depend on the methods used to measure, report, and synthesise them. For instance, some reviews highlight that education strategies can be useful to improve provider understanding — but without wider organisational or system-level change, they may struggle to deliver sustained transformation [ 19 , 44 ].

It is also worth highlighting the importance of the theory of change underlying the different interventions. Where authors of the systematic reviews draw on theory, there is space to discuss/explain findings. We note a distinction between theoretical and atheoretical systematic review discussion sections. Atheoretical reviews tend to present acontextual findings (for instance, one study found very positive results for one intervention, and this gets highlighted in the abstract) whilst theoretically informed reviews attempt to contextualise and explain patterns within the included studies. Theory-informed systematic reviews seem more likely to offer more profound and useful insights (see [ 19 , 35 , 40 , 43 , 45 ]). We find that the most insightful systematic reviews of MFIs engage in theoretical generalisation — they attempt to go beyond the data of individual studies and discuss the wider implications of the findings of the studies within their reviews drawing on implementation theory. At the same time, they highlight the active role of context and the wider relational and system-wide issues linked to implementation. It is these types of investigations that can help providers further develop evidence-based practice.

This overview has identified a small, but insightful set of papers that interrogate and help theorise why, how, for whom, and in which circumstances it might be the case that MFIs are superior (see [ 19 , 35 , 40 ] once more). At the level of this overview — and in most of the systematic reviews included — it appears to be the case that MFIs struggle with the question of attribution. In addition, there are other important elements that are often unmeasured, or unreported (e.g. costs of the intervention — see [ 40 ]). Finally, the stronger systematic reviews [ 19 , 35 , 40 , 43 , 45 ] engage with systems issues, human agency and context [ 18 ] in a way that was not evident in the systematic reviews identified in the previous reviews [ 8 , 9 ]. The earlier reviews lacked any theory of change that might explain why MFIs might be more effective than single ones — whereas now some systematic reviews do this, which enables them to conclude that sometimes single interventions can still be more effective.

As Nilsen et al. ([ 6 ] p. 7) note ‘Study findings concerning the effectiveness of various approaches are continuously synthesized and assembled in systematic reviews’. We may have gone as far as we can in understanding the implementation of evidence through systematic reviews of single and multi-faceted interventions and the next step would be to conduct more research exploring the complex and situated nature of evidence used in clinical practice and by particular professional groups. This would further build on the nuanced discussion and conclusion sections in a subset of the papers we reviewed. This might also support the field to move away from isolating individual implementation strategies [ 6 ] to explore the complex processes involving a range of actors with differing capacities [ 51 ] working in diverse organisational cultures. Taxonomies of implementation strategies do not fully account for the complex process of implementation, which involves a range of different actors with different capacities and skills across multiple system levels. There is plenty of work to build on, particularly in the social sciences, which currently sits at the margins of debates about evidence implementation (see for example, Normalisation Process Theory [ 52 ]).

There are several changes that we have identified in this overview of systematic reviews in comparison to the review we published in 2011 [ 8 ]. A consistent and welcome finding is that the overall quality of the systematic reviews themselves appears to have improved between the two reviews, although this is not reflected upon in the papers. This is exhibited through better, clearer reporting mechanisms in relation to the mechanics of the reviews, alongside a greater attention to, and deeper description of, how potential biases in included papers are discussed. Additionally, there is an increased, but still limited, inclusion of original studies conducted in low- and middle-income countries as opposed to just high-income countries. Importantly, we found that many of these systematic reviews are attuned to, and comment upon the contextual distinctions of pursuing evidence-informed interventions in health care settings in different economic settings. Furthermore, systematic reviews included in this updated article cover a wider set of clinical specialities (both within and beyond hospital settings) and have a focus on a wider set of healthcare professions — discussing both similarities, differences and inter-professional challenges faced therein, compared to the earlier reviews. These wider ranges of studies highlight that a particular intervention or group of interventions may work well for one professional group but be ineffective for another. This diversity of study settings allows us to consider the important role context (in its many forms) plays on implementing evidence into practice. Examining the complex and varied context of health care will help us address what Nilsen et al. ([ 6 ] p. 1) described as, ‘society’s health problems [that] require research-based knowledge acted on by healthcare practitioners together with implementation of political measures from governmental agencies’. This will help us shift implementation science to move, ‘beyond a success or failure perspective towards improved analysis of variables that could explain the impact of the implementation process’ ([ 6 ] p. 2).

This review brings together 32 papers considering individual and multi-faceted interventions designed to support the use of evidence in clinical practice. The majority of reviews report strategies achieving small impacts (normally on processes of care). There is much less evidence that these strategies have shifted patient outcomes. Combined with the two previous reviews, 86 systematic reviews of strategies to increase the implementation of research into clinical practice have been conducted. As a whole, this substantial body of knowledge struggles to tell us more about the use of individual and MFIs than: ‘it depends’. To really move forwards in addressing the gap between research evidence and practice, we may need to shift the emphasis away from isolating individual and multi-faceted interventions to better understanding and building more situated, relational and organisational capability to support the use of research in clinical practice. This will involve drawing on a wider range of perspectives, especially from the social, economic, political and behavioural sciences in primary studies and diversifying the types of synthesis undertaken to include approaches such as realist synthesis which facilitate exploration of the context in which strategies are employed. Harvey et al. [ 53 ] suggest that when context is likely to be critical to implementation success there are a range of primary research approaches (participatory research, realist evaluation, developmental evaluation, ethnography, quality/ rapid cycle improvement) that are likely to be appropriate and insightful. While these approaches often form part of implementation studies in the form of process evaluations, they are usually relatively small scale in relation to implementation research as a whole. As a result, the findings often do not make it into the subsequent systematic reviews. This review provides further evidence that we need to bring qualitative approaches in from the periphery to play a central role in many implementation studies and subsequent evidence syntheses. It would be helpful for systematic reviews, at the very least, to include more detail about the interventions and their implementation in terms of how and why they worked.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Before and after study

Controlled clinical trial

Effective Practice and Organisation of Care

High-income countries

Information and Communications Technology

Interrupted time series

Knowledge translation

Low- and middle-income countries

Randomised controlled trial

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Acknowledgements

The authors would like to thank Professor Kathryn Oliver for her support in the planning the review, Professor Steve Hanney for reading and commenting on the final manuscript and the staff at LSHTM library for their support in planning and conducting the literature search.

This study was supported by LSHTM’s Research England QR strategic priorities funding allocation and the National Institute for Health and Care Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King’s College Hospital NHS Foundation Trust. Grant number NIHR200152. The views expressed are those of the author(s) and not necessarily those of the NIHR, the Department of Health and Social Care or Research England.

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Boaz, A., Baeza, J., Fraser, A. et al. ‘It depends’: what 86 systematic reviews tell us about what strategies to use to support the use of research in clinical practice. Implementation Sci 19 , 15 (2024). https://doi.org/10.1186/s13012-024-01337-z

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Who Are You? The Art and Science of Measuring Identity

Table of contents.

As a shop that studies human behavior through surveys and other social scientific techniques, we have a good line of sight into the contradictory nature of human preferences. Today, we’re calling out one of those that affects us as pollsters: categorizing our survey participants in ways that enhance our understanding of how people think and behave.

Here’s the tension: On the one hand, many humans really like to group other humans into categories. Think, “Women are more likely to vote Democratic and men to vote Republican.” It helps us get a handle on big, messy trends in societal thought. To get this info, surveys need to ask each respondent how they would describe themselves.

On the other hand, most of us as individuals don’t like being put into these categories. “I’m more than my gender! And I’m not really a Republican, though I do always vote for them.” On top of that, many don’t like being asked nosy questions about sensitive topics. A list of the common demographic questions at the end of a survey can basically serve as a list of things not to raise at Thanksgiving dinner.

But our readers want to see themselves in our reports, and they want to know what people who are like them – and unlike them – think. To do that, it’s helpful for us to categorize people.

Which traits do we ask about, and why?

Unlike most Pew Research Center reports, where the emphasis is on original research and the presentation of findings, our goal here is to explain how we do this – that is, how we measure some of the most important core characteristics of the public, which we then use to describe Americans and talk about their opinions and behaviors.

To do so, we first chose what we judged to be the most important personal characteristics and identities for comparing people who take part in our surveys. Then, for each trait, we looked at a range of aspects: why and how it came to be important to survey research; how its measurement has evolved over time; what challenges exist to the accurate measurement of each; and what controversies, if any, remain over its measurement.

These considerations and more shape how we at Pew Research Center measure several important personal characteristics and identities in our surveys of the U.S. public. Here are some things to know about key demographic questions we ask:

  • Our main religion question asks respondents to choose from 11 groups that encompass 97% of the U.S. public: eight religious groups and three categories of people who don’t affiliate with a religion. Other, less common faiths are measured by respondents writing in their answer. Our questions have evolved in response to a rise in the share of Americans who do not identify with any religion and to the growing diversity in the country’s population. (Chapter 1)
  • Measuring income is challenging because it is both sensitive and sometimes difficult for respondents to estimate. We ask for a person’s “total family income” the previous calendar year from all sources before taxes, in part because that may correspond roughly to what a family computed for filing income taxes. To reduce the burden, we present ranges (e.g., “$30,000 to less than $40,000”) rather than asking for a specific number. (Chapter 2)
  • We ask about political party affiliation using a two-part question. People who initially identify as an independent or “something else” (instead of as a Republican or Democrat) and those who refuse to answer receive a follow-up question asking whether they lean more to the Republican Party or the Democratic Party. In many of their attitudes and behaviors, those who only lean to a party greatly resemble those who identify with it. (Chapter 3)
  • Our gender question tries to use terminology that is easily understood. It asks, “Do you describe yourself as a man, a woman or in some other way?” Amid national conversation on the subjects, gender and sexual orientation are topics on the cutting edge of survey measurement. (Chapter 4)
  • In part because we use U.S. Census Bureau estimates to statistically adjust our data, we ask about race and Hispanic ethnicity separately, just as the census does. People can select all races that apply to them. In the future, the census may combine race and ethnicity into one question. (Chapter 5)
  • A person’s age tells us both where they fall in the life span, indicating what social roles and responsibilities they may have, and what era or generation they belong to, which may tell us what events in history had an effect on their political or social thinking. We typically ask people to report just the year of their birth, which is less intrusive than their exact date of birth. (Chapter 6)

Each of these presents interesting challenges and choices. While there are widely accepted best practices for some, polling professionals disagree about how most effectively to measure many characteristics and identities. Complicating the effort is that some people rebel against the very idea of being categorized and think the effort to measure some of these dimensions is divisive.

It’s important that our surveys accurately represent the public

In addition to being able to describe opinions using characteristics like race, sex and education, it’s important to measure these traits for another reason: We can use them to make sure our samples are representative of the population. That’s because most of them are also measured in large, high-quality U.S. Census Bureau surveys that produce trustworthy national statistics. We can make sure that the makeup of our samples – what share are high school graduates, or are ages 65 or older, or identify as Hispanic, and so on – match benchmarks established by the Census Bureau . To do this, we use a tool called weighting to adjust our samples mathematically.

Some of the characteristics we’ll talk about are not measured by the government: notably, religion and party affiliation. We’ve developed an alternative way of coming up with trustworthy estimates for those characteristics – our National Public Opinion Reference Survey , which we conduct annually for use in weighting our samples.

You are who you say you are – usually

types of research studies slideshare

We mostly follow the rule that “you are who you say you are,” meaning we place people into whichever categories they say they are in. But that was not always true in survey research for some kinds of characteristics. Through 1950, enumerators for the U.S. census typically coded a person’s race by observation , not by asking. And pollsters using telephone surveys used respondents’ voices and other cues in the interview to identify their gender, rather than by asking them.

Nowadays, we typically ask. We still make judgments that sometimes end up placing a person in a different category than the one in which they originally placed themselves. For example, when we group people by religion, we use some categories that are not familiar to everyone, such as “mainline Protestant” for a set of denominations that includes the Episcopal Church, the United Methodist Church, the Presbyterian Church (U.S.A.) and others.

And we sometimes use respondents’ answers to categorize them in ways that go beyond what a single question can capture – such as when we use a combination of family income, household size and geographic location to classify people as living in an upper-, middle- or lower-income household .

Nosy but necessary questions

types of research studies slideshare

As much as people enjoy hearing about people like themselves, some find these types of personal questions intrusive or rude. The advice columnist Judith Martin, writing under the name Miss Manners, once provided a list of topics that “polite people do not bring into social conversation.” It included “sex, religion, politics, money, illness” and many, many more. Obviously, pollsters have to ask about many of these if we are to describe the views of different kinds of people (at Pew Research Center, we at least occasionally ask about all of these). But as a profession, we have an obligation to do so in a respectful and transparent manner and to carefully protect the confidentiality of the responses we receive.

If you’ve participated in a survey, it’s likely that the demographic questions came at the end. Partly out of concern that people might quit the survey prematurely in reaction to the questions, pollsters typically place these questions last because they are sensitive for some people and boring for most. Like other organizations that use survey panels – collections of people who have agreed to take surveys on a regular basis and are compensated for their participation – we benefit from a high level of trust that builds up over months or years of frequent surveys. This is reflected in the fact that we have fewer people refusing to answer our question about family income (about 5%) than is typical for surveys that ask about that sensitive topic. Historically, in the individual telephone surveys we conducted before we created the online American Trends Panel , 10% or more of respondents refused to disclose their family income.

One other nice benefit of a survey panel, as opposed to one-off surveys (which interview a sample of people just one time) is that we don’t have to subject people to demographic questions as frequently. In a one-off survey, we have to ask about any and all personal characteristics we need for the analysis. Those take up precious questionnaire space and potentially annoy respondents. In our panel, we ask most of these questions just once per year, since we are interviewing the same people regularly and most of these characteristics do not change very much.

Speaking of questions that Miss Manners might avoid, let’s jump into the deep end: measuring religion. (Or choose your own adventure by clicking on the menu.)

Choose a demographic category

Religion and religious affiliation, party affiliation, gender and sexual orientation, race and ethnicity, age and generations, acknowledgments.

This essay was written by Scott Keeter, Anna Brown and Dana Popky with the support of the U.S. Survey Methods team at Pew Research Center. Several others provided helpful comments and input on this study, including Tanya Arditi, Nida Asheer, Achsah Callahan, Alan Cooperman, Claudia Deane, Carroll Doherty, Rachel Drian, Juliana Horowitz, Courtney Kennedy, Jocelyn Kiley, Hannah Klein, Mark Hugo Lopez, Kim Parker, Jeff Passel, Julia O’Hanlon, Baxter Oliphant, Maya Pottiger, Talia Price and Greg Smith. Graphics were created by Bill Webster, developed by Nick Zanetti and produced by Sara Atske. Anna Jackson offered extensive copy editing of the finished product. See full acknowledgments .

All of the illustrations and photos are from Getty Images.

Find related content about our survey practices and methodological research.

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CORRECTION (February 20, 2024): An earlier version of this data essay misstated the share of Americans whose religion falls under the 11 main categories offered in the religious affiliation question. The correct share is 97%. This has now been updated.

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About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

At the Human Longevity Lab, studying methods to slow or reverse aging

Longevity Lab

  • Feinberg School of Medicine

The Potocsnak Longevity Institute at Northwestern University Feinberg School of Medicine has launched the Human Longevity Laboratory, a longitudinal,  cross-sectional study that will investigate the relationship between chronological age and biological age across different organ systems and validate interventions that may reverse or slow down the processes of aging.

“The relationship between chronological age (how many years old you are) and biological age (how old your body appears in terms of your overall health), and how they may differ, is key to understanding human longevity,” said Dr. Douglas Vaughan, director of the Potocsnak Longevity Institute. “Knowledge gained from this research may allow scientists to develop methods to slow the process of aging and push back the onset of aging-related disease, hopefully extending the ‘healthspan.’”

Anyone is eligible to participate in the Northwestern research study, but the scientists are focused on studying people who are disadvantaged with respect to biological aging, including those with HIV.

Our primary aim is to find ways to slow down the rate of aging in people that are aging too quickly and provide them with an opportunity to extend their healthspan.”

“We are particularly interested in bringing in people who are at risk for accelerated aging — people with chronic HIV infections, patients with chronic kidney disease, people exposed to toxic substances regularly (smoke and chemicals) and others,” Vaughan said. “Our primary aim is to find ways to slow down the rate of aging in people that are aging too quickly and provide them with an opportunity to extend their healthspan.”

The comprehensive research protocol includes assessments across various systems (cardiovascular, respiratory, neurocognitive, metabolic, and musculoskeletal), and novel molecular profiling of the epigenome. The studies will be performed at no cost to participants at Northwestern Medicine.

Over the next year, the team plans to enroll a diverse cohort representing individuals of all ages, ethnicities and socioeconomic backgrounds to form a picture of how aging affects all members of the population.

A participant’s results will be reviewed with them after their testing is complete. “That is information that might motivate some participants to improve their lifestyle, exercise more, lose weight or change their diet,” said Dr. John Wilkins, associate director of the Human Longevity Laboratory. Wilkins is also an associate professor of medicine in cardiology and of preventive medicine at the Feinberg School of Medicine, as well as a Northwestern Medicine physician.

Ultimately, the Human Longevity Laboratory will launch clinical trials designed to test therapeutics or interventions that might slow the velocity of aging.

View this site for more information on the study.

types of research studies slideshare

Dr. Vaughan plans to develop a network of sites duplicating the Human Longevity Laboratory with partners in the U.S. and globally. 

“We hope to clone our laboratory in terms of basic equipment and the protocol,” Vaughan said. “We intend to build a large database that is the most diverse and comprehensive in the world that will contribute significantly to our research.” Potential collaborative partners and sites have already been identified in Asia, Brazil, the Netherlands and in West Africa.

The Human Longevity Laboratory is part of the multi-center Potocsnak Longevity Institute , whose goal is to foster new discoveries and build on Northwestern’s ongoing research in the rapidly advancing science of aging. The Institute is funded by a gift from Chicago industrialist John Potocsnak and family.

“Aging is a primary risk factor for every disease affecting adults — including diabetes, arthritis, dementia, heart disease, diabetes, aging-related cancer, hypertension and frailty,” Vaughan said. “The biological processes that drive aging may be malleable. We think we can slow that process down, delay it, even theoretically reverse it. The curtain is being pulled back on what drives aging. We want to contribute to that larger discovery process.”

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Largest ever global study of COVID vaccines finds small but real link to neurological, blood, heart-related conditions

vaccines

Vaccines that protect against severe illness, death and lingering  long Covid  symptoms from a coronavirus infection were linked to small increases in neurological, blood, and heart-related conditions in the largest global  vaccine safety  study to date.

The rare events — identified early in the pandemic — included a higher risk of heart-related  inflammation  from  mRNA  shots made by Pfizer Inc., BioNTech SE, and Moderna Inc., and an increased risk of a type of blood clot in the brain after immunization with  viral-vector vaccines  such as the one developed by the University of Oxford and made by AstraZeneca Plc. 

The viral-vector jabs were also tied to an increased risk of  Guillain-Barre syndrome , a neurological disorder in which the immune system mistakenly attacks the peripheral nervous system.

More than 13.5 billion doses of Covid vaccines have been administered globally over the past three years,  saving  over 1 million lives in Europe alone. Still, a small proportion of people immunized were injured by the shots, stoking debate about their benefits versus harms.

The new research, by the Global Vaccine Data Network, was published in the journal Vaccine last week, with the data made available via interactive  dashboards  to show methodology and specific findings. 

The research looked for 13 medical conditions that the group considered “adverse events of special interest” among 99 million vaccinated individuals in eight countries, aiming to identify higher-than-expected cases after a Covid shot. The use of aggregated data increased the possibility of identifying rare safety signals that might have been missed when looking only at smaller populations.

Myocarditis , or inflammation of the heart muscle, was consistently identified following a first, second and third dose of mRNA vaccines, the study found. The highest increase in the observed-to-expected ratio was seen after a second jab with the Moderna shot. A first and fourth dose of the same vaccine was also tied to an increase in pericarditis, or inflammation of the thin sac covering the heart. 

Safety Signals

Researchers found a statistically significant increase in cases of  Guillain-Barre syndrome  within 42 days of an initial Oxford-developed ChAdOx1 or “Vaxzevria” shot that wasn’t observed with mRNA vaccines. Based on the background incidence of the condition, 66 cases were expected — but 190 events were observed. 

ChAdOx1 was linked to a threefold increase in cerebral venous sinus thrombosis, a type of blood clot in the brain, identified in 69 events, compared with an expected 21. The small risk led to the vaccine’s withdrawal or restriction in  Denmark  and multiple other countries. Myocarditis was also linked to a third dose of ChAdOx1 in some, but not all, populations studied.

Possible safety signals for  transverse myelitis  — spinal cord inflammation — after viral-vector vaccines were identified in the study. So was  acute disseminated encephalomyelitis  — inflammation and swelling in the brain and spinal cord — after both viral-vector and mRNA vaccines. 

Seven cases of acute disseminated encephalomyelitis after vaccination with the Pfizer-BioNTech vaccine were observed, versus an expectation of two.  

The adverse events of special interest were selected based on pre-established associations with immunization, what was already known about immune-related conditions and pre-clinical research. The study didn’t monitor for  postural orthostatic tachycardia syndrome , or POTS, that some research has  linked  with Covid vaccines.

Exercise intolerance, excessive fatigue, numbness and “brain fog” were among common symptoms identified in more than 240 adults experiencing chronic  post-vaccination  syndrome in a separate  study  conducted by the Yale School of Medicine. The cause of the syndrome isn’t yet known, and it has no diagnostic tests or proven remedies.

The Yale research aims to understand the condition to relieve the suffering of those affected and improve the safety of vaccines, said Harlan Krumholz, a principal investigator of the study, and director of the Yale New Haven Hospital Center for Outcomes Research and Evaluation. 

“Both things can be true,” Krumholz said in an interview. “They can save millions of lives, and there can be a small number of people who’ve been adversely affected.” 

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types of research studies slideshare

High levels of niacin linked to heart disease, new research suggests

High levels of niacin, an essential B vitamin, may raise the risk of heart disease by triggering inflammation and damaging blood vessels, according to new research.

The report, published Monday in Nature Medicine, revealed a previously unknown risk from excessive amounts of the vitamin, which is found in many foods, including meat, fish, nuts, and fortified cereals and breads.

The recommended daily allowance of niacin for men is 16 milligrams per day and for women who are not pregnant is 14 milligrams per day.

About 1 in 4 Americans has higher than the recommended level of niacin , said the study’s senior author, Dr. Stanley Hazen, chair of cardiovascular and metabolic sciences at the Cleveland Clinic’s Lerner Research Institute and co-section head of preventive cardiology at the Heart, Vascular and Thoracic Institute.

The researchers currently don’t know where to draw the line between healthy and unhealthy amounts of niacin, although that may be determined with future research.

"The average person should avoid niacin supplements now that we have reason to believe that taking too much niacin can potentially lead to an increased risk of developing cardiovascular disease,” Hazen said.

Currently, Americans get plenty of niacin from their diet since flour, grains and cereals have been fortified with niacin since the 1940s after scientists discovered that very low levels of the nutrient could lead to a potentially fatal condition called pellagra, Hazen said.

Prior to the development of cholesterol-lowering statins , niacin supplements were once even prescribed by doctors to improve cholesterol levels.

To search for unknown risk factors for cardiovascular disease, Hazen and his colleagues designed a multipart study that included an analysis of fasting blood samples from 1,162 patients who had come into a cardiology center to be evaluated for heart disease. The researchers were looking for common markers, or signs, in the patients’ blood that might reveal new risk factors. 

The research resulted in the discovery of a substance in some of the blood samples that is only made when there is excess niacin. 

Meat in grocery store

That finding led to two additional “validation” studies, which included data from a total of 3,163 adults who either had heart disease or were suspected of having it. The two investigations, one in the U.S. and one in Europe, showed that the niacin breakdown product, 4PY, predicted participants’ future risk of heart attack, stroke and death.

The final part of the study involved experiments in mice. When the rodents were injected with 4PY, inflammation increased in their blood vessels. 

The results are “fascinating” and “important,” said Dr. Robert Rosenson, director of metabolism and lipids for the Mount Sinai Health System in New York City.

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The newly detected pathway to heart disease might lead to the discovery of a medication that could reduce blood vessel inflammation and decrease the likelihood of major cardiovascular events, he added.

Rosenson hopes that the food industry will take note and “stop using so much niacin in products like bread. This is a case where too much of a good thing can be a bad thing.”

The new information could influence dietary recommendations for niacin, said Rosenson, who was not involved with the Cleveland Clinic research.

Scientists have known for decades that a person’s cholesterol level could be a major driver of heart disease, said Dr. Amanda Doran, an assistant professor of medicine in the division of cardiovascular medicine at the Vanderbilt University Medical Center.

Even when patients’ cholesterol levels were brought down, some continued to have a high risk of heart attacks and stroke, Doran said, adding that a 2017 trial suggested that the increased risk might be related to blood vessel inflammation.

Doran was surprised to learn that niacin could be involved in driving up the risk of heart disease.

“I don’t think anyone would have predicted that niacin would have been pro-inflammatory,” she said. “This is a powerful study because it combines a variety of techniques: clinical data, genetic data and mouse data.”

Finding the new pathway may allow future researchers to discover ways to reduce blood vessel inflammation, Doran said.

“It’s very exciting and promising,” she said.

Linda Carroll is a regular health contributor to NBC News. She is coauthor of "The Concussion Crisis: Anatomy of a Silent Epidemic" and "Out of the Clouds: The Unlikely Horseman and the Unwanted Colt Who Conquered the Sport of Kings." 

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The Impact of Menthol Cigarette Bans: A Systematic Review and Meta-Analysis

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Sarah D Mills, Snigdha Peddireddy, Rachel Kurtzman, Frantasia Hill, Victor Catalan, Jennifer S Bissram, Kurt M Ribisl, The Impact of Menthol Cigarette Bans: A Systematic Review and Meta-Analysis, Nicotine & Tobacco Research , 2024;, ntae011, https://doi.org/10.1093/ntr/ntae011

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This review investigates the impacts of banning the sale of menthol cigarettes at stores.

A systematic search of studies published in English up to November 2022 was conducted. The following databases were searched: PubMed/Medline, CINAHL, PsycINFO, Web of Science, and Embase, as well as a non-indexed journal. Studies evaluating either the impact of real-world or hypothesized menthol cigarette bans were included. Primary outcomes include tobacco use behaviors. Secondary outcomes include cigarette sales, retailer compliance, and the tobacco industry’s response to a menthol ban. Data on tobacco use behavior after a menthol ban were pooled using random-effects models. Two pairs of reviewers independently extracted data and assessed study quality.

Of the 964 articles that were identified during the initial search, 78 were included in the review and 16 were included in the meta-analysis. Cessation rates among menthol cigarette smokers were high after a menthol ban. Pooled results show that 24% (95% confidence interval [95% CI]: 20%, 28%) of menthol cigarette smokers quit smoking after a menthol ban, 50% (95% CI: 31%, 68%) switched to non-menthol cigarettes, 12% (95% CI: 3%, 20%) switched to other flavored tobacco products, and 24% (95% CI: 17%, 31%) continued smoking menthol cigarettes. Hypothesized quitting and switching rates were fairly close to real-world rates. Studies found the tobacco industry attempts to undermine menthol bans. National menthol bans appear more effective than local or state menthol bans.

Menthol cigarette bans promote smoking cessation suggesting their potential to improve public health.

Findings from this review suggest that menthol cigarette bans promote smoking cessation among menthol cigarette smokers and have the potential to improve public health.

  • tobacco use
  • menthol cigarettes
  • flavored tobacco products

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IMAGES

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  2. 2.3: Types of Research Studies and How To Interpret Them

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  3. Five Basic Types of Research Studies

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  4. Type Study

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  5. Different Types of Research

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  2. What is research

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COMMENTS

  1. Types of Research

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    Types of study design. Medical research is classified into primary and secondary research. Clinical/experimental studies are performed in primary research, whereas secondary research consolidates available studies as reviews, systematic reviews and meta-analyses. Three main areas in primary research are basic medical research, clinical research ...

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  10. What types of studies are there?

    The main types of studies are randomized controlled trials (RCTs), cohort studies, case-control studies and qualitative studies. Go to: Randomized controlled trials If you want to know how effective a treatment or diagnostic test is, randomized trials provide the most reliable answers.

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  13. 1.9: Types of Research Studies and How To Interpret Them

    A meta-analysis is a type of systematic review that goes one step further, combining the data from multiple studies and using statistics to summarize it, as if creating a mega-study from many smaller studies.4. However, even systematic reviews and meta-analyses aren't the final word on scientific questions.

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    Technological: This research looks for ways to improve efficiency in products, processes and production. Scientific: This research measures certain variables to predict behaviors, outcomes and impact. Example: A student working on a doctorate in education studies ways to increase student involvement in the classroom.

  17. 1.6: Types of Statistical Studies (4 of 4)

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    9 Exploratory Research This is carried out to investigate the possibilities of undertaking a particular research study. This type of research is also called feasibility study or pilot study It is usually carried out when a researcher wants to explore areas about which he/she has little or no knowledge. A small scale study is undertaken to decide if it is worth carrying out a detailed ...

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    Here are six common types of research studies, along with examples that help explain the advantages and disadvantages of each: 1. Meta-analysis. A meta-analysis study helps researchers compile the quantitative data available from previous studies. It's an observational study in which the researchers don't manipulate variables.

  20. (PPT) Types of research designs

    Part 1: quantitative designs. The objective of this chapter is to present the research design and statistical approach applied in this work. We explain the research philosophy used and justify the research approach. Develop a Research Design to Address a Research Question, Considering Strength and Weakness of Different Research Methods. High ...

  21. Chapter 10: Experimental Research

    Experimental Research • Experimental research is the most structured of all research. • When conducted well, experimental research studies can provide evidence for cause-effect relations. • Several experimental studies taken together can provide support for generalization of results.

  22. PPT

    Types of Research The types of research are determined by the aims of the researcher. A. Based on the Researcher's Objective Pure Research when the research is conducted solely to come up with new knowledge or to have a fuller understanding of a particular subject.

  23. 'It depends': what 86 systematic reviews tell us about what strategies

    Background The gap between research findings and clinical practice is well documented and a range of strategies have been developed to support the implementation of research into clinical practice. The objective of this study was to update and extend two previous reviews of systematic reviews of strategies designed to implement research evidence into clinical practice. Methods We developed a ...

  24. Who Are You? The Art and Science of Measuring Identity

    About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions.

  25. At the Human Longevity Lab, studying methods to slow or reverse aging

    "Knowledge gained from this research may allow scientists to develop methods to slow the process of aging and push back the onset of aging-related disease, hopefully extending the 'healthspan.'" Anyone is eligible to participate in the Northwestern research study, but the scientists are focused on studying people who are disadvantaged ...

  26. How dangerous are COVID vaccines? Global study

    The new research, by the Global Vaccine Data Network, was published in the journal Vaccine last week, with the data made available via interactive dashboards to show methodology and specific findings.

  27. High levels of niacin linked to heart disease, new research suggests

    High levels of niacin, an essential B vitamin, may raise the risk of heart disease by triggering inflammation and damaging blood vessels, according to new research.

  28. Impact of Menthol Cigarette Bans: A Systematic Review and Meta-Analysis

    Three studies from the same research group used simulation modeling to estimate the potential effects of a menthol ban in the US. 51-53 If a menthol ban were implemented in 2021, overall smoking prevalence was estimated to decline by 16% within 5 years post-ban. 52 Among non-Hispanic Black adults, smoking prevalence would decline by 25.3%. 53 ...

  29. How bubonic plague rewired the human immune system

    Skoglund points to a study he did of plague victims found in Somerset and Cumbria in the UK from around 4,000 years ago when Yersinia pestis had yet to develop the ability to be spread by fleas.