Research Methods in Psychology: Pros & Cons Coursework

True Experimental and Quasi-Experimental Designs

True experimental design includes purposively created groups, random assignments, and commonly measured outcomes. Factors that cannot be purposively manipulated are avoided. A quasi-experimental design is mostly applied in situations where the random assignment cannot be used. It focuses on independent variables that cannot be randomly assigned (Jackson, 2012).

Case Study of a Psychology Class

According to Jackson (2012), the professor should use a time series model, which falls under quasi-experimental design. In this approach, assessments are obtained from the treatment and the control groups prior to the intervention (Jackson, 2012). The series of observations provide the researcher with information on students’ performance over a period of time. As such, the approach is better compared to other quasi-designs that do not take into consideration the condition of the students prior to the experiment.

Single Group Design

The professor can also use the single group design. They can administer the test to an intact group of students. The group is then measured and observed. The professor should not randomly assign subjects to groups. In addition, they should not have an additional group for comparison (Jackson, 2012). The A-B-A-B design is recommended for this study as it helps the researcher to assess the condition of the students before and after the study. The reason is that it is implemented in stages. The professor will be able to determine how the condition affects the students. In addition, they will analyze the impacts of its withdrawal, helping them to make informed decisions.

Confounds during the Case Study

Maturation is one of the most common confounding factors associated with quasi-experimental and single designs (Jackson, 2012). In this case, it may occur if the behavior of the students at the beginning of the study differs from that reported at the end of the research. Another confounding factor is mortality. It can occur when a student who was part of the initial case study is unable to complete the research for some reason. Other confounding factors include selection, history, and regression (Jackson, 2012).

Cross-Sectional, Longitudinal, and Sequential Designs

Sequential design is a combination of cross-sectional and longitudinal models. Longitudinal design records individual differences, which can be accessed in the future. However, it has ‘lesser power’ to detect causal relationships compared to other experimental designs. The cross-sectional approach takes a short period of time to complete. However, it is prone to non-response issues and bias (Jackson, 2012). Sequential design helps the researcher to analyze large effects and trends over a short period of time. However, it is more expensive compared to the other designs.

Advantages of a Single Case Design

A researcher may use this design because of its various advantages. It enhances the success of treatment when compared to other approaches. In addition, only one group is needed. As such, it eliminates the need for standardized treatments (Jackson, 2012). It also makes it possible to observe long-lasting effects on participants.

Reversal Design

It is used to study the effects of treatment on the behavior of one individual. The individual is observed prior to the procedure to establish their baseline behavior (Jackson, 2012). The main advantage of this design is that it allows the researcher to establish a functional relationship between environmental manipulation and behavior. The major disadvantage is that it does not allow for the assessment of effects at the pre-intervention level.

Multiple-Baseline and Reversal Designs

Multiple-baseline design involves the study of behaviors of two or more persons. On its part, reversal design focuses on a single individual. In a multiple-baseline approach, the effect of experimental treatment is seen if a change in response occurs after intervention (Jackson, 2012).

Graphs and Interpretation of Data

A researcher may use graphs to visually analyze the difference between treatment phases (Jackson, 2012). A functional relationship between dependent and independent variables is inferred this way. The use of graphs provides a detailed picture of the results. In most cases, visual inspection of data generates results that statistical tests fail to show.

Reference

Jackson, S. (2012). Research methods and statistics: A Critical thinking approach (4th ed.). Belmont, CA: Wadsworth Cengage Learning.