Research methodologies part.1

IFMSA-Egypt Research Methodologies Manual

Research refers to a search for knowledge, it is an art of scientific investigation, by nature, people are inquisitive, so when the unknown confronts us, we wonder, and our inquisitiveness makes us probe and attain a full and fuller understanding of the unknown, this inquisitiveness is the mother of all knowledge and the method, which man employs for obtaining the knowledge of whatever the unknown, can be termed as research.

We can consider research as a movement from the known to the unknown, resembling a voyage of discovery, according to Clifford Woody, the research comprises defining and redefining problems, formulating hypotheses or suggested solutions; collecting, organizing, and evaluating data; making deductions and reaching conclusions; and at last, carefully testing the conclusions to determine whether they fit the formulating hypothesis, research is, thus, an original contribution to the existing stock of knowledge making for its advancement, it is the pursuit of truth with the help of study, observation, comparison, and experiment.

Therefore, the purpose of research is to find answers to questions through the application of scientific procedures, the principal aim of the research is to find out the truth which has not been discovered as yet, though each research study has its specific purpose, we may think of research objectives as falling into several following broad groupings:

● To gain familiarity with a phenomenon or to achieve new insights into it (exploratory or formulative research studies).

● To portray accurately the characteristics of a particular individual, situation, or group (descriptive research studies).

● To determine the frequency with which something occurs or with which it is associated with something else (diagnostic research studies).

● To test a hypothesis of a causal relationship between variables (hypothesistesting research studies).

Many motives may be the underlying reason for conducting research, the following:

 Desire to get a research degree along with its consequential benefits.

 Desire to face the challenge in solving unsolved problems, i.e., concern over practical problems, initiates research.

 Desire to get intellectual joy from doing some creative work.

 Desire to be of service to society.

 Desire to get respectability.

However, this is not a full list of factors motivating people to undertake research studies, many more factors such as directives of the government, employment conditions, curiosity about new things, desire to understand causal relationships, social thinking, and awakening.

The basic types of research are as follows:

Descriptive vs. Analytical:

Descriptive research includes surveys and fact-finding inquiries of different kinds, the major purpose of descriptive research is the description of the state of affairs as it exists at present, the main characteristic of this method is that the researcher has no control over the variables; he can only report what has happened or what is happening, the methods of research utilized in descriptive research are survey methods of all kinds, including comparative and correlational methods, in analytical research, on the other hand, the researcher has to use facts or information already available and analyze these to make a critical evaluation of the material.

Applied vs. Fundamental:

Research can either be applied (or action) research or fundamental (to basic or pure) research. Applied research aims at finding a solution for an immediate problem facing a society or an industrial/business organization, whereas fundamental research is mainly concerned with generalizations and with the formulation of a theory, “Gathering knowledge for knowledge‖s sake is termed ―pure‖ or ―basic‖ research.” Therefore, the central aim of applied research is to discover a solution for some pressing practical problem, whereas basic research is directed towards finding information that has a broad base of applications and thus, adds to the already existing organized body of scientific knowledge.

Quantitative vs. Qualitative:

Quantitative research is based on the measurement of quantity or amount, it applies to phenomena that can be expressed in terms of quantity, qualitative research, on the other hand, is concerned with qualitative phenomena, i.e., phenomena relating to or involving quality or kind, this type of research aims at discovering the underlying motives and desires, using in-depth interviews for the purpose, other techniques of such research are word association tests, sentence completion tests, story completion tests, and similar other projective techniques. Qualitative research is especially important in the behavioral sciences where the aim is to discover the underlying motives of human behavior. Through such research we can analyze the various factors which motivate people to behave in a particular manner or which make people like or dislike a particular thing.

Conceptual vs. Empirical:

Conceptual research is that related to some abstract idea(s) or theory, it is generally used by philosophers and thinkers to develop new concepts or to reinterpret existing ones, on the other hand, empirical research relies on experience or observation alone, often without due regard for system and theory, it is data-based research, coming up with conclusions which are capable of being verified by observation or experiment, we can also call it the experimental type of research. In such research, the researcher must first provide himself with a working hypothesis or guess as to the probable results, he then works to get enough facts (data) to prove or disprove his hypothesis. He then sets up experimental designs which he thinks will manipulate the persons or the materials concerned to bring forth the desired information, therefore, this research is characterized by the experimenter‖s control over the variables under study and his deliberate manipulation of one of them to study its effects, empirical research is appropriate when the proof is sought that certain variables affect other variables in some way. Evidence gathered through experiments or empirical studies is today considered to be the most powerful support possible for a given hypothesis.

Some Other Types of Research:

All other types of research are variations of one or more of the above-stated approaches, based on either the purpose of research, the time required to accomplish research, the environment in which research is done, or based on some other similar factor. From the point of view of time, we can think of research either as one-time research or longitudinal research. In the former case, the research is confined to a single period, whereas in the latter case the research is carried on over several periods. Research can be field-setting research, laboratory research, or simulation research, depending upon the environment in which it is to be carried out. The research may be exploratory, whose objective is to develop hypotheses rather than testing them, or it may be formalized, which is description of the types of research emphasizes that there are two basic approaches to research: with substantial structure and with specific hypotheses to be tested. Research can also be classified as conclusion-oriented and decisionoriented. While doing conclusion-oriented research, a researcher is free to pick up a problem, redesign the inquiry as he proceeds, and is prepared to conceptualize as he wishes. Decision-oriented research is always for the need of a decision maker and the researcher in this case is not free to embark upon research according to his inclination.

The above

The quantitative approach: it involves the generation of data in a quantitative form which can be subjected to rigorous quantitative analysis formally and rigidly. This approach can be further sub-classified into:

 Inferential approach: The purpose of this approach is to form a database from which to infer characteristics or relationships of the population. This

usually means survey research where a sample of the population is studied (questioned or observed) to determine its characteristics, and it is then inferred that the population has the same characteristics.

 Experimental approach: The experimental approach is characterized by much greater control over the research environment, and in this case, some variables are manipulated to observe their effect on other variables.

 Simulation approach: simulation approach involves the construction of an artificial environment within which relevant information and data can be generated. This permits observation of the dynamic behavior of a system (or its sub-system) under controlled conditions.

The qualitative approach: it is concerned with the subjective assessment of attitudes, opinions, and behavior. Research in such a situation is a function of the researcher‖s insights and impressions. Such an approach to research generates results either in a non-quantitative form or in a form which is not subjected to rigorous quantitative analysis. Generally, the techniques of focus group interviews, projective techniques, and depth interviews are used.

RESEARCH METHODS VERSUS METHODOLOGY

It seems important to elaborate on the difference between research methods and research methodology. Research methods may be understood as all those methods/techniques that are used for the conduction of research. Research methods or techniques, thus, refer to the methods the researchers use in performing research operations. In other words, all those methods which are used by the researcher during studying his research problem are termed research methods.

Since the object of research, particularly applied research, is to solve a given problem, the available data and the unknown aspects of the problem have to be related to each other to make a solution possible. Keeping this in view, research methods can be put into the following three groups:

1. In the first group, we include those methods which are concerned with the collection of data. These methods will be used where the data already available are not sufficient to arrive at the required solution.

2. The second group consists of those statistical techniques which are used for establishing relationships between the data and the unknowns.

3. The third group consists of those methods which are used to evaluate the accuracy of the results obtained. Research methods falling in the last two groups of the above-stated are generally taken as the analytical tools of research.

Type Methods Techniques

1. Library Research

i. Analysis of historical records

ii. Aanlysis of documents

Recording of notes, Content analysis, Tape and film listening and analysis.

Statistical compilations and manipulations, Reference and abstract guide, Content analysis

2. Field Research

i. Non- Observational behavior

participant direct observation

ii. Participant observaion

scales, use of score cards, etc.

Interactional recording, possible use of tape recorders, photographic techniques.

iii. Mass observations Recording mass behavior, interview using independent observers in public places.

iv. Mail Questionnaire

Identification of social and economic background of participants.

v. Opinionnaire Use of attitude scales, projective techniques, use of socioeconomic scales

vi. Personal interview

Interviewer uses a detailed schedule with open and closed questions

vii. Focused interview Interviewer focuses attention upon a given experience and is effects

viii. Group interview

ix. Telephone survey

Small groups of responders are interviewed simultaneously

Used as a survey technique for information and for discerning opinion; may also be used as a follow up for a questionnaire

x. Case study and life history

Cross sectional collection of data for intensive analysis, longitudinal collection of data of intensive character

3. Laboratory research

Small group study of random behaviour, play and role analysis

Use of audio-visual recording devices, use of observers, etc.

According to the table above, we can say that methods are more general. It is the methods that generate techniques. However, in practice, the two terms are taken as interchangeable and when we talk of research methods we do, by implication, include research techniques within their compass.

Research methodology is a way to systematically solve the research problem. It may be understood as a science of studying how research is done scientifically. In methodology, we study the various steps that are adopted by a researcher in studying his research problem along with the logic behind them. The researcher must know not only the research method and techniques but also the methodology.

Researchers not only need to know how to develop certain indices or tests, how to calculate the mean, the mode, the median or the standard deviation or chi-square, and how to apply particular research techniques, but they also need to know which of these methods or techniques, are relevant and which are not, and what would they mean and indicate and why.

Researchers also need to understand the assumptions underlying various techniques and they need to know the criteria by which they can decide that certain techniques and procedures will apply to certain problems and others will not.

All this means that the researcher must design his methodology for his problem as the same may differ from problem to problem. For example, an architect, who designs a building, has to consciously evaluate the basis of his decisions, i.e., he has to evaluate why and on what basis he selects a particular size, number, and location of doors, windows, and ventilators, uses particular materials and not others and the like. Similarly, in research, the scientist has to expose the research decisions to evaluation before they are implemented. He has to specify clearly and precisely what decisions he selects and why he selects them so that they can be evaluated by others also.

Based on what has been stated, we can say that research methodology has many dimensions and research methods constitute a part of the research methodology. The scope of the research methodology is wider than that of research methods.

As a result, when we talk of research methodology, we not only talk of the research methods but also consider the logic behind the methods we use in the context of our research study and explain why we are using a particular method or technique and why we are not using others so that

research results are capable of being evaluated either by the researcher himself or by others. Why a research study has been undertaken, how the research problem has been defined, in what way and why the hypothesis has been formulated, what data have been collected and what particular method has been adopted, why a particular technique of analyzing data has been used and a host of similar other questions are usually answered when we talk of research methodology concerning a research problem or study.

Basic Research

Research is classified into two major groups based on the application of its findings: Basic Research and Applied (Clinical) Research (Polit & Hungler, 1983).

Basic research is the purest form of research which entails gathering information relating to a specific topic to extend the knowledge of the such topic (Burns & Grove, 1987).

Basic research is also undertaken to formulate a theory or refine one, approve the models within the theory, and generate novel relationships between concepts within the theory, thus producing new theories.

Basic research is commonly used in medical research to define and further clarify physiological and psychological behaviors and pathways of humans. Basic research findings tend not to have any practical use. However, because the findings are theoretical, they can be generalized to a variety of settings (Polit & Hungler, 1983).

Clinical Research

The definition of clinical research might appear to be self-evident; however, some researchers have narrowly defined clinical research to refer to clinical trials (i.e., intervention studies in human patients), while others have broadly defined it as any research design that studies humans (patients or subjects) or any materials taken from humans.

This latter definition may even include animal studies, the results of which are more or less directly applicable to humans. For example, in 1991, Ahrens included the following in the definition of clinical research: studies on the mechanisms of human disease; studies on the management of the disease; in vitro studies on materials of human origin; animal models of human health and disease; the development of new technologies; the assessment of health care delivery; and field surveys.

The Association of American Medical Colleges Task Force on Clinical Research defined clinical research as "a component of medical and health research intended to produce knowledge essential for understanding human disease, preventing and treating illness, and promoting health. Clinical research embraces a continuum of studies involving interaction with patients, diagnostic clinical materials or data, or populations, in any of these categories: disease mechanisms; translational research; clinical knowledge; detection; diagnosis and natural history of the disease; therapeutic interventions including clinical trials; prevention and health promotion; behavioral research; health services research; epidemiology; and community-based and managed care-based research".

1ry and 2ry Research

Medical research is classified into primary and secondary research. Clinical, also referred to as experimental, studies are performed in primary research, whereas secondary research consolidates available studies as narrative reviews, systematic reviews, and meta-analyses. Three main areas in primary research are basic medical research, clinical research, and epidemiological research.

Basic research includes fundamental research in fields shown in Figure x. In almost all studies, at least one independent variable is varied, whereas the effects on the dependent variables are investigated. Clinical studies include observational studies and interventional studies and are subclassified as in Figure x.

Knowing common research terminology helps you understand how to read and interpret scholarly journal articles so you can more effectively apply the results to real world human performance. The following are basic research terms and definitions.

1. Abstract: a brief overview of a research study.

2. Constitutive definition: the basic, dictionary meaning.

3. Construct: a term that describes a human variable that is not directly measurable (e.g., motivation, self-esteem). Also known as a psychological construct.

4. Control group: in experiments, the one that doesn't get the treatment.

5. Correlational study: a type of research design that depicts a relationship between variables, but not necessarily one of cause-effect.

6. Data: information. can be numbers or words. plural form of datum. the "data show" not "shows."

7. Dependent variable: the quality you are observing.

8. Descriptive study: research design that describes "what is" (e.g., a survey.)

9. Experiment: a research design used to find relationships between variables.

10. Experimental group: the one that get the treatment.

11. External validity: how generalizable the results are outside of the study as it concerns other populations and locations.

12. Independent variable: the one you are manipulating. The effect of (independent, such as smoking) on ( dependent, lung cancer).

13. Measures of central tendency: averages, e.g., the mean.

14. Measures of variability: spread, e.g., standard deviation.

15. Operational definition: how a term is used in a study.

16. Population: all of a group of interest.

17. Random sample: everybody had the same chance of being assigned to any group. sometimes confused with who you ran into by chance.

18. Research: a systematic, objective way to generate facts or knowledge.

19. Research design: the plan or method for finding out what you want to know. experiments, correlations, descriptive studies.

20. Sample: a smaller group that represents a population of interest.

21. Significance: two meanings: significance of the study means why it is important. Also see statistical significance.

22. Standard deviation: a measure of spread. the average deviation of a group of scores from the mean.

23. Statistical significance: an important finding that did not likely happen by chance. p<.05 means that there were less than 5 chances in 100 that the result would have happened randomly.

24. Statistics: mathematical tools based on the normal curve used to analyze data. researchers must match statistics with research designs.

25. T-score: a standard score on the normal curve where the mean is assigned "50" with deviations of "10". Allows a more simple interpretation of student achievement.

26. T-test: a parametric statistical tool that compares differences between the means of two groups; assumptions for use include normal distribution and at least interval data.

27. The extent to which: a favorite phrase of researchers that means "how much". implies ranges and probabilities and avoids absolutes.

28. Validity: accuracy. the extent to which a test or study measures what it is supposed to measure.

29. Z-score: a standard score on the normal curve. Each z-score is counted as "1" from the mean, plus or minus.

RESEARCH PROCESS

One of the first decisions a researcher makes is whether to conduct a quantitative or a qualitative study. Researchers generally work within a framework consistent with their field (nursing practice, management, education…etc.). When little is known about a topic, an appropriate frame is identified, and then the researcher moves from the beginning point of a study (the question) to the endpoint (the answer to that question) in a logical sequence of steps known as the research process. (Burns & Grove, 1987).

The research process can be divided into five main phases:

Phase one: The conceptual phase.

Phase two: The design and planning phase.

Phase three: The empirical phase.

Phase four: The analytic phase.

Phase five: The dissemination phase.

Phase one: The conceptual phase

The activities involved in this phase include thinking, reading, rethinking, theorizing, and reviewing ideas with colleagues or advisors. During this phase, the researcher calls on such skills as creativity, deductive reasoning, insight, and grounding in previous research on the study topic.

This phase includes the following steps:

 Step one: Stating a research problem: A researcher starts by moving from a broad area of interest to a more specific problem that tells exactly what he/she is going to study.

 Step two: Defining the research purpose: This is called the aim of the study. It explains the following: why the research question is important and how the answer to this question will be utilized in practice.

 Step three: Reviewing related literature: The review of literature is an essay in which the researcher relates the existing concepts, theories, research methods, and findings to his study question and purpose. Literature review provides the researcher with:

- Ideas for defining concepts.

- Means for formulating operational definitions.

- Relevant theories.

- Related facts, issues, and research.

- Prior findings.

- Instruments for their measurements.

 Step four: Formulating hypothesis and defining variables: This step entails writing statements about an expected relationship between the study variables. Some researchers develop hypotheses, while others test hypotheses (experimental).

Phase two: The design and planning phase

 Step five: Selecting the research design: The research design is a systematic and controlled plan for finding answers to the study question. It offers a map for organizing the sample through data analysis.

 Step six: Selecting population and sampling: Once the researcher has formulated his study question, reviewed his/her literature and decided on a plan for doing the study, he/she is now ready to choose the study population and sample.

Phase three: The empirical phase

 Step seven: Developing data collection tool and collecting data: The data selection tool of choice depends on:

- Research type.

- Nature of population and sample.

- Data collector's preparation.

- Time for data collection.

- Nature of data to be collected.

Phase four: The analytical phase

 Step eight: Data analysis: It is the process of taking the data that have been collected apart and reorganizing them again in relation to the study questions, objectives, and/or hypothesis.

 Step nine: Interpreting and discussing the results: The study findings are explained, discussed, and elaborated on in relation to previous findings and what has been written about it in the literature.

 Step ten: Writing a conclusion and recommendation: Finally, the main study results are organized in a specific conclusion that answers the study question and meets its objectives. This is followed by recommendations that are based on the study conclusion, suggesting directions for future lines of research.

Phase five: The Dissemination phase

 Step eleven: Communicating conclusions: This is done through writing a detailed research report and publishing it in the relevant literature.

RESEARCH DESIGN

Introduction

The formidable problem that follows the task of defining the research problem is the preparation of the design of the research project, popularly known as the “research design”.

Decisions regarding what, where, when, how much, and by what means concerning an inquiry or a research study constitute a research design. “A research design is the arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to the research purpose with economy in procedure.”

The research design is the conceptual structure within which research is conducted; it constitutes the blueprint for the collection, measurement, and analysis of data.

As such the design includes an outline of what the researcher will do from writing the hypothesis and its operational implications to the final analysis of data.

More explicitly, the design decisions happen to be in respect of:

(i) What is the study about?

(ii) Why is the study being made?

(iii) Where will the study be carried out?

(iv) What type of data is required?

(v) Where can the required data be found?

(vi) What periods will the study include?

(vii) What will be the sample design?

(viii) What techniques of data collection will be used?

(ix) How will the data be analyzed?

(x) In what style will the report be prepared?

Keeping in view the above-stated design decisions, one may split the overall research design into the following parts:

 The sampling design which deals with the method of selecting items to be observed for the given study.

 The observational design which relates to the conditions under which the observations are to be made.

 The statistical design which concerns the question of how many items are to be observed and how the information and data gathered are to be analyzed.

 The operational design which deals with the techniques by which the procedures specified in the sampling, statistical and observational designs can be carried out.

From what has been stated above, we can state the important features of a research design as under:

 It is a plan that specifies the sources and types of information relevant to the research problem.

 It is a strategy specifying which approach will be used for gathering and analyzing the data.

 It also includes the time and cost budgets since most studies are done under these two constraints.

In brief, the research design must, at least, contain:

 A clear statement of the research problem.

 Procedures and techniques to be used for gathering information.

 The population to be studied.

 Methods to be used in the processing and analyzing of data.

In this section we will discuss a research design consisting of:

Study design

Variables and measurement scales

Research instrument

Sampling strategy

STUDY DESIGNS

What is a study design?

A study design is a framework or a set of methods and procedures used to collect and analyze data on variables specified in a particular research problem. Many types of study designs present each with its advantages and limitations. The choice of study design dictates the question that will be addressed, the analytic strategy, and the strength of the causal inferences that can be made.

Study designs classifications

Primary research

● Observational Studies

● Interventional Studies

Secondary Research

● Systematic Review

● Analysis-Meta

● Narrative Review

PRIMARY RESEARCH

Descriptive studies

Observe and describe the behavior of subjects and patterns of outcome occurrence.

No Control group, Not Repeatable.

 Case series

 Case reports

 Cross-sectional studies

Analytical studies

Measure the association between exposure and outcome.

 Cross-sectional studies

 Cohort studies

 Case control studies

 Ecological studies

Cross-sectional study

A type of study that measures the exposure and outcome in a population at a single time point (A snapshot of a population). Begins by selecting a sample population and then obtaining data to classify all individuals in the sample as either having or not having the health outcome.

Aim

 Describe population characters

 Prevalence

 Hypothesis generation

Types

 Descriptive survey study: Describe the characteristics of a population or phenomenon being studied.

 Repetitive survey study: Asking the same questions at different times points to new sample people on each occasion.

 Longitudinal survey study: Asking or interviewing the same population at different time points, and to study rapid fluctuations in behaviors, thoughts, and emotions.

Advantages

 Inexpensive

 Short duration (no follow-up)

 More representative than case series and case reports.

Disadvantages

 Unsuitable for diseases of short duration

 Unsuitable for rare diseases.

 Bias

 Not possible to know if the outcome followed the exposure or if the exposure followed the outcome.

Cohort studies

What’s a cohort?

A cohort is a group of persons, usually 100 or more in size, who share common characteristics e.g. smokers, or people born in the same year.

What’s a cohort study?

A study of two groups which, a group of people are exposed to a particular risk factor and a control group that is not exposed. The two groups are free of the outcome and both are followed up for a duration of time to determine the different rates of outcomes.

Types

 Prospective cohort

At the time that the investigators begin enrolling subjects and collecting baseline exposure information, none of the subjects have developed any of the outcomes of interest. Then, follow up of subjects to determine changes in a certain outcome.

 Retrospective cohort

Retrospective studies are conducted after some people have already developed the outcomes of interest, yet the investigation starts from the exposure towards outcome as usual.

Advantages over prospective cohort:

Inexpensive

Less time consuming

 Retro-prospective cohort

A combination of the two types

The study starts in the past and continues in the future.

Advantage

 Can study multiple outcomes of the same exposure

 Good for rare exposure

 Eliminate recall bias.

 Well-defined relationship between an exposure and an outcome (no reverse causality).

Disadvantage

 Long period of follow-up.

 Not good for rare diseases.

 Attrition: loss of participants due to death, migration, disinterest.

Case-control studies

A study that compares patients who have an outcome of interest (cases) with patients who do not have the outcome (controls), and looks back to compare how frequently the exposure to a risk factor is present in each group to determine the relationship between the risk factor and the outcome.

Types

 Non-matched case-control

-The simplest form (and old).

-Select subjects with the disease.

-Select controls without the disease.

 Matched case-control

Select subjects with the disease and match them with control with similar characteristics (e.g. sex, age, weight).

 Nested case-control studies

A case-control study in which cases and controls are drawn from the population in a fully enumerated cohort.

Advantages

 Inexpensive (compared to cohort studies).

 Good for rare diseases.

 Useful for studying multiple exposures to the same diseases.

Disadvantages

 Selection, recall, information bias.

 Not good for rare exposures.

 Not useful in multiple outcomes.

Ecological studies

An observational study is defined as examining the association between an exposure and an outcome, namely at the population or group level, rather than the individual level.

They are inexpensive and easy to carry out, using routinely collected data, but they are prone to bias and confounding.

Advantages

 The purpose of the study is to monitor population health so that public health strategies may be developed.

 The purpose of the study is to make large-scale comparisons, e.g. comparisons between countries.

Disadvantages

 Inaccuracy of data.

 Inability to control confounders.

Case report

A detailed report of symptoms, signs, diagnosis and treatment of an individual patient.

Usually describe an unusual or novel occurrence:

 Unusual side effects to therapy.

 Previously unreported disease.

 Unique use of diagnostic tests.

 Unique or rare feature of a disease.

 Unusual presentation.

 New diagnosis method.

Advantages

 Can be shared for medical and educational purposes.

 Useful in rare conditions

 Generate potential hypotheses for future studies.

Disadvantages

 Data cannot be quantified, also, there is no comparison so cannot be generalized, except in specific patients.

Case series

A study that describes a set of patients who show similar symptoms or outcomes. Almost as case reports, but differ in number. There is no control group.

Types

 Case series

 Retrospective case series: uses existing data such as medical records.

 Consecutive case series: includes all eligible patients identified by the researchers during the study registration period. The patients are treated in the order in which they are identified.

Advantages

 Can be shared for medical and educational purposes.

 Useful in rare conditions.

 Generate potential hypotheses for future studies.

Disadvantages

 Data cannot be quantified and there is no comparison so cannot be generalized except in specific patients.

Longitudinal studies Definition

A type of correlational research in which researchers observe and collect data on a number of variables without trying to influence those variables.

The opposite of a longitudinal study is a cross-sectional study. While longitudinal studies repeatedly observe the same participants over a period of time, cross-

sectional studies examine different samples (or a “cross-section”) of the population at one point in time.

Types of longitudinal studies

They are generally observational, however, may also be experimental. Some of these are briefly discussed below:

1. Repeated cross-sectional studies

where study participants are largely or entirely different on each sampling occasion.

2. Prospective studies

Cohort panels:

wherein some or all individuals in a defined population with similar exposures or outcomes are considered over time.

Representative panels:

where data is collected for a random sample of the population..

Linked panels:

wherein data collected for other purposes is tapped and linked to form individualspecific datasets.

3. Retrospective studies

Designed after at least some participants have already experienced events that are of relevance; with data for potential exposures in the identified cohort being collected and examined retrospectively.

Advantages of longitudinal studies

 The ability to identify and relate events to particular exposures, and to further define these exposures with regards to presence, timing and chronicity.

 Allow researchers to follow their subjects in real time. This means you can better establish the real sequence of events, allowing you insight into cause-and-effect relationships.

 Allow repeated observations of the same individual over time. This means any changes in the outcome variable cannot be attributed to differences between individuals.

 Prospective longitudinal studies (as cohort studies ) eliminate the risk of recall bias, or the inability to correctly recall past events.

 Ability to correct for the “cohort effect” that is, allowing for analysis of the individual time components of cohort (range of birth dates), period (current time), and age (at point of measurement) and to account for the impact of each individually.

Disadvantages

 Time-consuming and often more expensive than other types of studies, so they require significant commitment and resources to be effective.

 Since longitudinal studies repeatedly observe subjects over a period of time, any potential insights from the study can take a while to be discovered

 Attrition, which occurs when participants drop out of a study, is common in longitudinal studies and may result in invalid conclusions.

 The potential for inaccuracy in conclusion if adopting statistical techniques that fail to account for the intra-individual correlation of measures.

Interventional studies (clinical trials)

Definition

Scientific experiment that is done prospectively in clinical research in which researchers introduce an intervention and study the effect of it either as a:

1. Diagnostic: such as new methods or tests for diagnosis of a disease or condition.

2. Preventive: such as vaccines.

3. Therapeutic: as any new treatment for a specific condition or detection of the effect of present treatment on another condition.

Importance and Advantages

 Provide a causative relationship between exposure to a certain risk factor and outcome.

 Test and evaluate the safety and the efficacy of the intervention.

 Control over risk assignment.

 Provide strong evidence ( Randomized control trials are gold standard studies by their high internal validity ).

Disadvantages

 Expensive.

 Time consuming.

 Sometimes expose participants to potential harm.

 May be difficult, inappropriate or unethical.

Differences between it and observational study

Observational studies

Researchers merely document the presence of exposure(s) and outcome(s) as they occur, without trying to alter the course of natural events.

Interventional studies

The researcher actively interferes with nature – by performing an intervention in some or all study participants – to determine the effect of exposure to the intervention on the natural course of events

Non-controlled studies (single-arm studies)

The simplest trial design, in which a sample of individuals with the targeted medical condition is given the experimental therapy and then followed over time to observe their response without a control group.

Responses could theoretically be due to the efficacy of the treatment, a placebo effect of an inefficacious therapy, or to a spontaneous or natural history improvement.

When to use non-controlled design

1. When the objective of the trial is to obtain preliminary evidence of the efficacy of the treatment.

2. To collect additional safety data.

3. When the available patient pool is limited and thus it is not optimal to randomize many participants to a control arm.

4. When the natural history of the disease is well understood, placebo effects are minimal or nonexistent, placebo control is not ethically desirable.

5. May be the only (or one of few) options for trials evaluating therapies for which placebos are not ethical and options for controlled trials are limited

6. Have been commonly implemented in oncology trials (Oncology trials often employ a dose at or near the maximum tolerated dose (MTD, known from Phase I trials) to deliver the maximum effect and thus frequently employ single dose trials.)

N.B

When designing single-arm trials, it is important to clearly define the goal or hypothesis of interest.

Limitations of Single-Arm Design

 NOT generally used as confirmation of efficacy.

 The interpretation of the trial results can be complicated despite the design simplicity.

 The inability to distinguish between the effect of the treatment, a placebo effect, and the effect of natural history.

 Would not be good choices for trials investigating treatments for chronic pain because of the large placebo effect in these trials.

It is also difficult to interpret the response without a frame of reference for comparison.

Controlled trials

Definition

Studies that contain more than one group (interventional group VS control group) to know what would have happened to patients if they had not received the test treatment or if they had received a different treatment known to be effective.

Major Purpose of Control Group

Allow discrimination of patient outcomes (for example, changes in symptoms, signs, or other morbidity) caused by the test treatment from outcomes caused by other factors, such as the natural progression of the disease, placebo effect, natural history, observer or patient expectations, or other treatment.

Types of control

1. placebo concurrent control

Placebo is considered as an inactive treatment that is similar to the form of intervention in route, color and shape, sometimes it‖s called a ―sugar pill‖, In fact, it may be in a pill or tablet form, or it may be an injection or a medical device.

2. No-treatment concurrent control

3. Dose-response concurrent control

Which the control is a different dose from the intervention

4. Active (positive) concurrent control

The control is an active treatment for comparison between the two treatments.

5. External control (including historical control)

In which the control group is from a population other than the interventional population (from another setting), it may be in the same time period of the study or earlier time period (historical control).

6. Multiple control groups

In this type, there are multiple types of control such as use of active control and placebo. This design may be useful for active drug comparisons where the relative potency of the two drugs is not well established, or where the purpose of the trial is to establish relative potency.

Types of Controlled Trials

1. According to randomization

Randomization:

Assurance that subject populations are similar in intervention and control groups is best attained by randomly dividing a single sample population into groups that receive the intervention or control treatments.

A. Randomized-controlled trials (The golden standard studies):

 A study design that randomly assigns participants into an experimental group or a control without following a “random” procedure.

 The only expected difference between the control and experimental groups in a randomized controlled trial (RCT) is the outcome variable being studied.

B.

Non - Randomized trials:

 In this design, participants are assigned to different intervention arms without following a “random” procedure but a quasi random manner.

 May be based on the investigator's convenience or whether the participant can afford a particular drug or not.

 Susceptible to bias with patients in the two groups being potentially dissimilar.

 The validity of the results obtained is low.

When to Use a Non-Randomized Trial

 When the act of random allocation may reduce the effectiveness of the intervention.

 Occurs when the effectiveness of the intervention depends on the participant‖s active participation which is influenced by their beliefs and preferences).

 When it would be unethical to do random allocation.

 When it is impractical to do random allocation (e.g. cost or convenience factors).

 When there are legal or political obstacles to random allocation.

2. According to blinding

Blinding:

A procedure in which one or more parties in a trial are kept unaware of which treatment arms participants have been assigned to, i.e. which treatment was received in order to avoid bias.

A. Unblinded or open label:

All are aware of the treatment the participant receives.

B. Single blind or single-masked:

Only the participant is unaware of the treatment they receive.

C. Double blind or double-masked:

The participant and the clinicians / data collectors are unaware of the treatment the participant receives.

D. Triple blind: Participants, clinicians / data collectors and outcome adjudicators / data analysts are all unaware of the treatment the participant receives.

SECONDARY STUDY DESIGNS

Secondary study designs are study designs that use pre-existing data. Rather than collect data by conducting a research project, a researcher analyzes, interprets, and draws conclusions from past research studies. There are two types of secondary study designs: non-systematic reviews, and systematic reviews. Systematic reviews are then further categorized into qualitative systematic reviews, and meta-analyses.

1. Non-systematic reviews

A non-systematic review is a summary of other published studies. The reviewer interprets the studies and draws conclusions from them, rather than using external criteria. It is also known as a literature review, or a narrative review.

Non-systematic reviews are well suited for general topics or broad coverage of a field as they usually cover a wide range of issues within a given topic. A non-systematic review can be identified by the general title and the absence of the words such as study of, clinical trial, or effects of.

Non-systematic reviews are typically written by the expert of a certain field, rather than by experts in research methodology. Because of this, a biased selection of the resources cited is bound to occur. This is known as selection bias. Another common bias in nonsystematic reviews is failure to include research that conflicts with the beliefs or opinions of the expert.

2. Systematic reviews

The term “systematic review” is a general term describing a look back at multiple published reports on a single topic. Its purpose is to answer one or more focused questions relevant to the topic.

Systematic reviews are actually a type of scientific investigation. What sets them apart from clinical trials is that the “subjects” are a cluster of previous published studies that meet strict criteria.

Investigators comb through bibliographic databases and then eliminate studies that do not meet certain criteria, like sufficient sample size or appropriate randomization. The use of a search strategy is what makes a review “systematic,” rather than just a nonsystematic (literature) review.

There are two types of systematic reviews: qualitative systematic reviews, and metaanalyses.

3. Qualitative Systematic reviews

In a qualitative systematic review, the primary studies are reviewed and summarized without attempts at statistical analysis. Here, the authors use an explicit and systematic method to search for, critically appraise and combine the conclusions from individual studies on the topic of interest.

What sets apart a qualitative systematic review from a meta-analysis is that it doesn‖t view data from a numerical perspective. Rather, it looks at the conclusions derived, and summarizes them into one larger study.

Selection is based on predefined inclusion and exclusion criteria, quality is assessed, and data are abstracted in a standardized format. This means that the published result contains no numerical data; it contains conclusions derived from said data.

By explicitly stating how the evidence was found, how it was appraised or validated, and which studies were excluded (and why), qualitative systematic reviews eliminate many of the biases inherent in non-systematic reviews.

4. Meta-analysis

A meta-analysis is often a component of a systematic review. Meta-analyses combine the results of several studies about a topic as if they were from one large study.

Meta-analyses may be included in qualitative systematic reviews, since their numerical data can be abstracted. The studies included in a meta-analysis are found using the same rigorous search methodology as that used for systematic reviews.

In a sense, meta-analysis is a data-oriented, statistically grounded research study about research studies. Like other research studies, meta-analyses use relative risk, odds ratios, confidence intervals, and data that are used in reports of clinical trial research.

Pilot study

A pilot study is a sort of trial study. Its purpose is to determine whether the proposed experiment for a research problem is appropriate, ethical, and effective. It utilizes conditions approximating those of the larger, proposed study but with a smaller number of subjects over a much shorter period of time.

The pilot can provide information about the difficulties surrounding the recruitment of participants and appropriateness and ethicality of data collection procedures. In addition, the pilot can be used to assess the efficacy of the proposed research instruments.The pilot can also obtain preliminary estimates of many statistics useful in informing sample size calculations for future investigations. In certain circumstances, the pilot can even produce preliminary answers to certain research questions.

If successful, the results of a pilot study can be helpful in convincing potential stakeholders and other researchers that the proposed research is feasible and is likely to yield results.

If the investigator concludes that the study is defective or too time-costly, they may consider modifications to render the study appropriate. Examples of such modifications include expanding the scope of the problem, adding additional study sites, and altering the study design.

Experimental Observational

Study Design Randomized Control Trial Crosssectional Cohort Case-Control

Study Population Highly selected population, highly controlled environment

Diverse population observed in a range of setting

Primary Use Demonstrating efficacy of an intervention

Screening hypotheses; prevalence studies

Assessing association between multiple exposures and outcomes over time

Assessing associations between exposures and rare outcome

Analysis Straightforward

Internal Validity

External Validity

High

Sophisticated multivariate techniques may be required to account for confounding

Low

Low-Moderate

High

HOW TO CHOOSE YOUR STUDY DESIGN

As stated before, research designs are broadly divided into observational studies (i.e., cross-sectional; case–control and cohort studies) and experimental studies (randomized control trials, RCTs).

There are several approaches that we can use to answer a specific research question; Each design has a specific role, and each has both pros and cons. It is crucial for researchers to be aware of the role of each study design, their respective pros and cons, and the inherent risk of bias with each design.

While there are many quantitative study designs available to researchers, the final choice is dictated by two key factors:

1. First, by the specific research question. If the question is one of ―prevalence‖ (disease burden) then the ideal is a cross-sectional study; if it is a question of ―harm‖–a case–control study; prognosis–a cohort and therapy–a RCT.

2. Second, by what resources are available to you. This includes budget, time, feasibility re‐patient numbers and research expertise.

Considering these factors will help the researcher choose the optimal design to address the research question, as all these factors will severely limit the choice.

VARIABLES

A variable is a characteristic or property that takes on different values in different persons, places or things. Variables are measurable by data. The variables determined may be for any study at any level, be it local, national, or international. In medical research, they assess changes in health or disease situations.

Variables generally fall in one of two categories:

1. Qualitative (discrete) variables.

2. Quantitative (continuous) variables.

3. Hybrid variable.

Qualitative (categorical) variables

Qualitative variables are variables where the magnitude or size of the characteristic or attribute as the same cannot be measured. They are classified by counting the number of people having or reporting the same characteristic or attribute, and not by measurement. In this case, there is only one variable: the number of people. Qualitative variables are discrete in nature, so they are sometimes referred to as discrete variables.

Qualitative variables are typically used in clinical trials, usually to collect information on the action of the drug or its efficacy. They are also used in surveys, as part of pilot studies.

Qualitative variables are particularly suited to gaining understanding of people‖s lived worlds, that is, how people experience and view, believe and think, aspire and assess the world about them. They may be used to generate hypotheses that can then be tested by other variables. Qualitative variables are also very vital in pilot studies and also may be used to collect data that help to interpret statistical material.

Examples of qualitative variables are blood groups, the presence of disease, and the type of drug used to treat a disease. Blood group is a qualitative variable because it cannot be measured. Rather, it is classified into four groups: A, B, AB, or O.

Qualitative variables can be further classified into:

1. Nominal.

2. Ordinal.

3. Binary.

1. Nominal variables

Nominal variables represent names or categories. Examples include blood type, gender, marital status, hair color, etiology, and presence versus absence of a risk factor or disease, and vital status. Nominal variables represent the weakest level of measurement as they have no intrinsic order or other mathematical properties and allow only for qualitative classification or grouping.

2. Ordinal variables

Ordinal variables are considered to be semiquantitative. They are similar to nominal variables in that they are composed of categories, but their categories are arranged in a meaningful sequence (rank order), such that successive values indicate more or less of some quantity (i.e., relative magnitude). Typical examples of ordinal variables include socioeconomic status, tumor classification scores, New York Heart Association (NYHA) functional class for angina or heart failure, disease severity, birth order, perceived level of pain, and all opinion survey scores.

3. Binary variables

Binary variables are variables that can take two possible values. Some examples of binary variables include male or female, true or false, yes and no, etc. There are two types of binary variables: opposite and conjunct. Opposite binary variables, most common, are binary variables whose two possible values are opposites like yes or no, and true and false. Conjunct binary variables are variables whose possible values aren‖t exactly opposite like political preference in the US. where you can prefer democrats or republicans.

Quantitative (numerical) variables

Quantitative variables are measurable variables. They possess a magnitude; they can be measured numerically. Quantitative variables are measured on an interval or on a ratio scale.

The quantitative data are also known as continuous data, since each individual has one measurement from a continuous spectrum or range such as body temperature from 35–42°C.

The characteristic may be measurable in whole numbers and fractions such as chest circumference: 33 cm, 34.5 cm, 35.2 cm, 36 cm, 37.3 cm and so on or it may be measurable or countable in discrete whole numbers only, such as pulse rate, cholesterol, blood pressure, ESR, blood sugar, etc.

Data in quantitative variables are collected by using laboratory tests or patient response questionnaires and surveys that ask the respondent how much or how many. Quantitative data may be displayed graphically or summarized and otherwise analyzed through the use of descriptive and/or inferential statistics.

Quantitative variables can be further classified into:

 Continuous.

 Discrete.

Continuous variables

Continuous variables are variables that can theoretically take on any value along a continuum. They are characterized by the measurement of ranges of variables. For example, “age” is a continuous variable because, theoretically at least, someone can be any age. “Income,” “weight,” and “height” are other examples of continuous variables.

Discrete variables (Counts)

Discrete variables (e.g., number of dental caries, number of white cells per cubic centimeter of blood, number of readers of medical journals, or other count-based data) can take on only whole numbers. Data acquired over time or space may be expressed in discrete numbers.

Scientific experiments have several types of variables. The independent and dependent variables are the ones usually plotted on a chart or graph, but there are other types of variables you may encounter.

Type Independent Variable Dependent Variable Controlled Variable

Definition The independent variable is the one condition that you change in an experiment.

The dependent variable is the variable that you measure or observe.

The dependent variable gets its name because it is the factor that is dependent on the state of the independent variable.

A controlled variable or constant variable is a variable that does not change during an experiment.

Example In an experiment measuring the effect of temperature on solubility, the independent variable is temperature.

In the experiment measuring the effect of temperature on solubility, solubility would be the dependent variable.

In the experiment measuring the effect of temperature on solubility, controlled variables could include the source of water used in the experiment, the size and type of containers used to mix chemicals, and the amount of mixing time allowed for each solution.

Additionally, Extraneous and Confounding Variables

Extraneous variables are all variables, which are not the independent variable, but could affect the results of the experiment. Extraneous variables that are not recognized until the study is in process or recognized but cannot be controlled are called confounding variables. Confounding variables can sometimes be measured, but in most cases, these variables cannot be measured and should then be included in the study limitations as they‖re bound to hinder the interpretation of the findings. (Burns & Grove, 2001).

MEASUREMENT & MEASUREMENT LEVELS

Understanding measurement and measurement levels are important in the data collection phase of research. At this phase, the research variables must be operationally defined. An operational definition, not to be confused with a conceptual definition, denotes how variables will be observed and measured. Measurement is the process of assigning numbers or quantification of data. This allows the researcher to categorize the variables and assign them definite quantities which enable comparisons to be made. (Nieświadomy & Bailey, 2018).

The type of statistical analysis that can be done on data is determined by its level of measurement (measurement scale). There are 4 levels of measurement: nominal, ordinal, interval, and ratio.

LEVELS OF MEASUREMENT

The nominal measurement level entails data being categorized or named. Each category must be distinct from the other and include all possible data that fall into it. The number of categories depends on the data being collected, there could be 2 or 20 categories. Numbers are assigned to data in this measurement level by counting the frequency or percentage of objects within each category. An example of nominal data is gender. Gender can only be measured at the nominal level as there are only two options, male or female. A researcher would then calculate the number of males and females in his study and assign each a percentage. The nominal level is the lowest level of measurement and is the least precise for that matter. (Nieświadomy & Bailey, 2018)

Data that can be ranked in addition to being categorized is at the ordinal measurement level. At the ordinal level, while data can be ranked, the exact quantitative difference between each rank cannot be specified. For instance, stress levels of people can be categorized into mild, moderate, and severe. You could confidently conclude that individuals with severe stress levels are more stressed than those with moderate stress levels. However, you cannot determine the exact difference between stress levels. This measurement scale usually yields frequencies, percentages, and distributions of data. (Nieświadomy & Bailey, 2018)

Interval measurement scales include data that not only can be categorized and ranked but also the exact difference between ranks can be identified. Temperature and test scores are among the common interval data. If body temperature was being measured, each different reading would constitute a category, as 37.2 is a category while 37.4 is a different category. The researcher can then identify the exact difference between these two categories. (Nieświadomy & Bailey, 2018)

In the ratio measurement level, in addition to categorizing and ranking data and identifying exact differences between ranks, there is an absolute zero. The zero point on a scale indicates the absence of the variable being measured. For instance, if a researcher wanted to determine the number of certain medication requests by patients, the number of requests can be zero. This data would be considered ratio data. The ratio measurement scale is considered the highest and most precise. (Nieświadomy & Bailey, 2018)

Some data, while cannot be measured to be zero, are considered ratio data. Height and weight are prominent examples. Most researchers consider weight as a ratio, although you cannot weigh someone and get zero as a result.

Data can always be converted from a higher scale to a lower one but never the other way around. For example, the number of contractions a pregnant woman experiences during a specific time can be converted from the ratio scale, where it is an exact number and a zero point could be identified, to the ordinal scale, where it could be either few, a moderate amount, or plenty. However, researchers rarely convert data from a higher scale to a lower one as precision is lost. (Nieświadomy & Bailey, 2018)

Choosing the appropriate measurement scale for your study depends on two main points: the type and operational definition of data being collected, and the degree of precision required to answer the research question or test the hypothesis. If a researcher is concerned about the precision of the data, an interval or ratio measurement scale should be used. But, if ranked or categorized data will be sufficient to answer the research question or test the hypothesis, a nominal or ordinal measurement scale would be used. Moreover, some variables can only be measured at a certain level. For example, gender can only be measured at the nominal level, as a person can only be male or female. (Nieświadomy & Bailey, 2018)

REASEARCH INSTRUMENTS

When a researcher reaches the data collection stage of the research process, there are several key items that the researcher should identify. These items include who will collect the data, when will the data be collected, at what setting will the data be collected, what data will be collected, and how will the data be collected. (Nieświadomy & Bailey, 2018)

The terms research instrument and data collection tool are synonyms. The data collection tool, or instrument, is the mean by which the researcher will collect data relevant to the variable of inquiry. There are several types of data collection tools that a researcher would choose from. The suitable data collection tool varies depending on the variable of inquiry, the research question or hypothesis, and the study design. Different types of data collection tools include questionnaires and surveys, observation methods, interviews, etc. (Nieświadomy & Bailey, 2018).

Questionnaires

A questionnaire is simply a sum of questions that a researcher formulates to gather data about a certain variable. A questionnaire is considered a self-report data collection tool. Questionnaires have a variety of formats which makes them very versatile. And this versatility is perhaps the reason it is the most common data collection tool (Phillips & Stawarski, 2016). Questionnaires can be used to collect different types of data: opinions, knowledge levels, attitudes, beliefs, feelings, ideas, and perceptions as well as factual information about the respondents (Nieświadomy & Bailey, 2018).

Types of Questions

The questions in a questionnaire are the most essential part. Utilizing the appropriate question type is the main step in formulating an effective questionnaire. There are several types of questions used in questionnaires: demographic, closed-ended, open-ended, contingency, and filler questions. These types are not exclusive though. For instance, a demographic question can be either open-ended or close-ended. (Nieświadomy & Bailey, 2018).

1. Demographic questions

Do not necessarily provide data about the variable. Rather, they‖re used to collect data about the characteristics of the sample. These characteristics, called demographic variables or attribute variables, include age, educational level, religious affiliation, etc.

Besides the fact that almost all questionnaires have a target demographic of respondents, data collected from these questions can be used to study the relationship between respondent characteristics and their responses to questions about other variables in the study. (Nieświadomy & Bailey, 2018).

2. Close-ended questions

Are questions where the researcher provides a set of predetermined answers for the respondent to choose from. A close-ended question can have two options like a true or false question (Birmingham, 2016).

It can include a check list where the respondent selects all items that apply. Other forms of close-ended questions are multiple choice questions and matching questions (Phillips & Stawarski, 2016). Close-ended questions are the most structured form of questions in a questionnaire. To ensure that a close-ended question is structured correctly, the answer choices must be collectively exhaustive and mutually exclusive (Nieświadomy & Bailey, 2018).

Collectively exhaustive means all possible answers to the question are included in the choices. Mutually exclusive means every choice distinct from the others, they don‖t overlap.

3. Open-ended questions

These are questions where the researcher allows the respondent to answer in their own words rather than choosing from multiple answers. Open-ended questions include essay questions and fill-in-the-blanks (Phillips & Stawarski, 2016). Openended questions can be presented with close-ended questions where the respondent would be able to add any explanation or thought of theirs after answering the close-ended question.

4. Contingency questions

These are questions that do not apply to all respondents. Whether a respondent should answer such a question is dependent (contingent) on their answer to its preceding question. “How long was the last hospitalization period” is an example of a contingent question; if the respondent has been hospitalized before then they can answer this question but if the respondent hasn‖t been hospitalized then the question doesn‖t apply to them. (Nieświadomy & Bailey, 2018).

5. Filler questions

These are questions that a researcher would include in their questionnaire to deter respondents from giving answers that they think the researcher is looking for.

Filler questions are questions that the researcher has no direct interest in but are included to drain some emphasis from the main subject to achieve more honest answers. (Nieświadomy & Bailey, 2018).

Advantages

Questionnaires have numerous advantages as a data collection tool. A questionnaire is generally a quick and inexpensive tool to collect data from a large sample. It is also easier to test for the reliability and validity of a questionnaire than other data collection tools. Administration of questionnaires is less time-consuming than interviews or observation and it is relatively easy to obtain data from a sample with a prevalent geographical status. The anonymity of respondents is easily maintained. Finally, questionnaires are easily analyzed compared to other research tools. (Nieświadomy & Bailey, 2018).

Disadvantages

However, there are several key disadvantages to questionnaires. For once, response rates to questionnaires are typically low. Also, as the researcher is unable to explain items in the questionnaires that may be misunderstood, contrary to interviews, respondents might fail to answer some of the questions. Some respondents may also provide socially acceptable answers rather than their honest opinions.

Finally, because of the nature of the questionnaires, respondents must be literate and free of any physical disability that would prevent them from answering the questionnaire. (Nieświadomy & Bailey, 2018).

Interviews

Interviews, although not as common as questionnaires, is a very useful and prominent tool for collecting data. An interview is a process where an interviewer directly obtains information from a respondent through a face-to-face meeting, a telephone call, or, more commonly now than ever, an online call or meeting (Phillips & Stawarski, 2016). Interviews are used to collect factual information about people and their opinions, attitude, and beliefs on a certain topic.

Types 1. Structured interview

It is where an interviewer asks respondents specific questions with little to no variation in the questions asked between respondents.

2. Unstructured interviews

They allow the interviewer to ask additional questions and seek further information from each respondent. (Birmingham, 2016).

Advantages

Interviews have a few advantages which set them separate from other research instruments.For once, response rates in interviews are relatively high compared to questionnaires. Also, and most importantly, more in-depth information can be gathered as well as nonverbal behaviors.

Disadvantages

However, there are several key downsides to interviews. Interviews are very time-consuming, unstructured interviews especially. Also, you need to be trained interviewers to be able to collect sufficient and relevant data.

An interviewer might misinterpret respondents‖ answers or respondents might give socially correct answers or be influenced by the interviewer rather than giving honest answers. Besides, setting up and scheduling an interview is a costly and difficult task. (Nieświadomy & Bailey, 2018).

Observation Methods:

Observation data collection methods are used to gather data by observing a selected sample during a period. Observations can vary on how structured they are.

Rather than two options, structured or unstructured, it‖s a range where most observations lie somewhere between the two ends.

1. Structured observation

It is when the observer has prior knowledge of the phenomenon they‖re trying to observe.

The observer will also have a checklist of the expected behaviors where he\she will record the frequency of each behavior.

2. Unstructured observation

It is where the observer records the events that occur without any prior knowledge of what would be observed. Thus, most observation methods lie between these two ends. The observation method is designed with some predetermined behaviors to observe but the observer is instructed to record any additional behaviors. (Nieświadomy & Bailey, 2018).

Time/event sampling:

Observation methods can also be classified into event sampling or time sampling. This refers to the criteria to which the period of observation will be executed. Event sampling is observing an entire event. Time sampling is observing behaviors during a certain period. Either of the classifications would be chosen based on the research aim.

According to the observer:

Observation methods can also be classified depending on the relationship between the observer and the subjects. There are two main types: participant and non-participant observer.

1. Participant observer

It is an observer who is involved in several interactions with the subject. That observer is in most cases a co-worker of the subjects being observed.

2. Non-participant observer

It is an observer who does not interact with subjects.

Participant

and non-participant observers

A. Overt observer

can be overt or covert.

It is an observer who the subjects know, prior to data collection, is observing them for research purposes.

B. Covert observer

It is an observer who the subjects, prior to data collection, are unaware of. Covert observation is rarely ethical. One example of an ethical covert observation is observing public behavior like the number of people who wear their seatbelts. (Nieświadomy & Bailey, 2018

Other Data Collection Tools:

Other data collection tools include attitude scales, physiological tests, and psychological tests.

1. Attitude scales

These are self-report data collection tools that ask respondents to rate their attitudes about a certain topic on a predetermined scale. After the responses are collected, each respondent is given a score. The researcher then can compare between the respondents‖ attitudes using these scores.

One of the most common attitude scales used is the Likert scale. Likert scale, named after its developer Rensis Likert, usually consists of five to seven responses to each item ranging from strongly agree to strongly disagree. (Nieświadomy & Bailey, 2018).

2. Physiological tests

They involve collecting physical data from subjects to measure a specific variable. Physiological measure of best quality is its accuracy and objectivity. For instance, observing facial expressions and body movements, and measuring heart rate and oxygen saturation to measure the response of subjects to acute pain is considered a physiological measure. (Nieświadomy & Bailey, 2018).

3. Psychological Tests

They are used to measure the personality traits, needs, or values of people. There are many types of psychological tests. One major type is personality inventories. Personality inventories collect data from subjects by asking them direct questions or requesting responses to statements provided for the trait being measured.

Another major type is the projective technique which aims to eliminate the disadvantage of self-report psychological measures, socially acceptable answers rather than genuine ones. In this technique, the subject exhibits stimuli that are designed to be ambiguous then the subject is asked to describe this stimulus. The responses represent the feelings of the respondent. (Nieświadomy & Bailey, 2018).

Using Existing Research Instruments:

During the literature review stage of the research process, the researcher might find a research instrument that is appropriate to use in his study. The use of an existing research instrument connects the present study with previous studies on the variable inquiry.

To use an existing research instrument, you must contact the developer of the instrument to obtain permission to use the instrument to avoid violating the developer‖s copyright. Moreover, there are numerous sources of published research instruments that are available for fair use by researchers. Some of the best sources are published compilations of instruments where each instrument is explained extensively and knowledge about the validity and reliability and the psychometric analysis of each instrument is presented, e.g: the DASS-21 scale in psychiatry research. (Nieświadomy & Bailey, 2018).

What makes a good research instrument:

 Validity

The degree to which a test or measuring instrument measures what it tends to measure

 Reliability

Means the extent to which a research instrument is dependable, consistent and stable.

N.B :

-The instrument can be considered reliable but not valid

-Reliability is less important than validity

-The most useful instrument which is both valid and reliable



Usability (practicability)

The ease with which the administration and interpretation of an instrument by patients and the scoring / interpretation by the researchers without undue expenditure of time, money and effort.

Factors of usability determination:

 Ease of administration.

 Ease of scoring.

 Ease of interpretation and application.

 Low cost.

 Proper mechanical make up.

RESEARCH it out!