SectionA_group6_Kaiwalya Kumar Misra_13PGP025 Session 4

Types of Research Studies

There are four major classifications of research designs. These include observational research, correlational research, true experiments, and quasi-experiments. Each of these is discussed below.

1)    Observational research: There are many types of studies which could be defined as observational research including case studies, ethnographic studies, ethological studies, etc. The primary characteristic of each of these types of studies is that phenomena are being observed and recorded. Often times, the studies are qualitative in nature. For example, a psychological case study would entail extensive notes based on observations of and interviews with the client. Surveys are often classified as a type of observational research.

2)    Correlational research: In general, correlational research examines the covariation of two or more variables. For example, the early research on cigarette smoking examine the covariation of cigarette smoking and a variety of lung diseases. These two variable, smoking and lung disease were found to co-vary together. Correlational research is considered type of observational research as nothing is manipulated by the experimenter or individual conducting the research. For example, the early studies on cigarette smoking did not manipulate how many cigarettes were smoked. The researcher only collected the data on the two variables. Nothing was controlled by the researchers.

3)    True Experiments: The true experiment is often thought of as a laboratory study. However, this is not always the case. A true experiment is defined as an experiment conducted where an effort is made to impose control over all other variables except the one under study. It is often easier to impose this sort of control in a laboratory setting. Thus, true experiments have often been erroneously identified as laboratory studies.

To understand the nature of the experiment, we must first define a few terms:

  1. Experimental or treatment group – this is the group that receives the experimental treatment, manipulation, or is different from the control group on the variable under study.
  2. Control group – this group is used to produce comparisons. The treatment of interest is deliberately withheld or manipulated to provide a baseline performance with which to compare the experimental or treatment group’s performance.
  3. Independent variable – this is the variable that the experimenter manipulates in a study. It can be any aspect of the environment that is empirically investigated for the purpose of examining its influence on the dependent variable.
  4. Dependent variable – the variable that is measured in a study. The experimenter does not control this variable.
  5. Random assignment – in a study, each subject has an equal probability of being selected for either the treatment or control group.
  6. Double blind – neither the subject nor the experimenter knows whether the subject is in the treatment of the control condition.

Now that we have these terms defined, we can examine further the structure of the true experiment. First, every experiment must have at least two groups: an experimental and a control group. Each group will receive a level of the independent variable. The dependent variable will be measured to determine if the independent variable has an effect. As stated previously, the control group will provide us with a baseline for comparison.

4)    Quasi Experiment: quasi-experiment is an empirical study used to estimate the causal impact of an intervention on its target population. Quasi-experimental designs typically allow the researcher to control the assignment to the treatment condition, but using some criterion other than random assignment (e.g., an eligibility cut-off mark). In some cases, the researcher may have no control over assignment to treatment condition.

Quasi-experiments are subject to concerns regarding internal validity, because the treatment and control groups may not be comparable at baseline. With random assignment, study participants have the same chance of being assigned to the intervention group or the comparison group. As a result, differences between groups on both observed and unobserved characteristics would be due to chance, rather than to a systematic factor related to treatment (e.g., illness severity). Randomization itself does not guarantee that groups will be equivalent at baseline. Any change in characteristics post-intervention is likely attributable to the intervention. With quasi-experimental studies, it may not be possible to convincingly demonstrate a causal link between the treatment condition and observed outcomes.


Types of Sampling Procedures


A sample consists of a subset of the population. Any member of the defined population can be included in a sample. A theoretical list (an actual list may not exist) of individuals or elements who make up a population is called a sampling frame. There are five major sampling procedures.

1)    Convenience: Volunteers, members of a class, individuals in the hospital with the specific diagnosis being studied are examples of often used convenience samples. This is by far the most often used sample procedure. It is also by far the most biases sampling procedure as it is not random (not everyone in the population has an equal chance of being selected to participate in the study). Thus, individuals who volunteer to participate in an exercise study may be different that individuals who do not volunteer.

2)    Simple random sample: In this method, all subject or elements have an equal probability of being selected. There are two major ways of conducting a random sample. The first is to consult a random number table, and the second is to have the computer select a random sample.

3)    Systematic sample: It is conducted by randomly selecting a first case on a list of the population and then proceeding every nth case until your sample is selected. This is particularly useful if your list of the population is long. For example, if your list was the phone book, it would be easiest to start at perhaps the 17th person, and then select every 50th person from that point on.

4)    Stratified sampling: In a stratified sample, we sample either proportionately or equally to represent various strata or subpopulations. For example if our strata were states we would make sure and sample from each of the fifty states. If our strata were religious affiliation, stratified sampling would ensure sampling from every religious block or grouping. If our strata were gender, we would sample both men and women.

5)    Cluster sampling: In cluster sampling we take a random sample of strata and then survey every member of the group. For example, if our strata were individual’s schools in the St. Louis Public School System, we would randomly select perhaps 20 schools and then test all of the students within those schools.


Sampling Problems


There are several potential sampling problems. When designing a study, a sampling procedure is also developed including the potential sampling frame. Several problems may exist within the sampling frame.

1)    Missing elements – individuals who should be on your list but for some reason are not on the list. For example, if my population consists of all individuals living in a particular city and I use the phone directory as my sampling frame or list, I will miss individuals with unlisted numbers or who cannot afford a phone.

2)    Foreign elements: Elements which should not be included in my population and sample appear on my sampling list. Thus, if I were to use property records to create my list of individuals living within a particular city, landlords who live elsewhere would be foreign elements. In this case, renters would be missing elements.

3)    Duplicates: These are elements who appear more than once on the sampling frame. For example, if I am a researcher studying patient satisfaction with emergency room care, I may potentially include the same patient more than once in my study. If the patients are completing a patient satisfaction questionnaire, I need to make sure that patients are aware that if they have completed the questionnaire previously, they should not complete it again. If they complete it more than once, their second set of data represents a duplicate.

-Kaiwalya Kumar Misra (13pgp025) 





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