Module 3 Assignment 1

Quantitative Research

The quantitative research design involves the collection of numeric measurement data to explain, predict, or control the area of research interest. For this type of research, the data analysis is always statistical. The types of quantitative research using statistical data analysis and the quantitative approach are descriptive, correlational, causal comparative, and experimental.

Descriptive behavioral science research involves the collection of measurement data in order to test hypotheses or answer predetermined questions regarding participants of a study. Unlike qualitative studies, quantitative studies are numeric, and the data is usually collected through questionnaires, surveys, polls, observations, and interviews. Some examples of descriptive studies in behavioral sciences include how jurists spend their nonjury times, how a new psychological test will fare in a highly competitive market, or how lawyers feel about trial news coverage.

Correlational research attempts to determine to what degree and whether a relationship exists between two or more variables. Caution must be exercised with correlation studies as there might be a positive or negative correlation between variables, which does not mean that one variable causes a reaction in the other variable. Essentially, correlation does not imply causation. For example, ice-cream sales and drownings are correlated in nearly every country of the world for every month of the year. However, undoubtedly, neither ice-cream sales nor drownings cause the other to occur. Instead, those two phenomena are likely related to a third variable that, as you may have already concluded, would be the weather. As temperature increases throughout the globe, so do ice-cream consumption and swimming (as swimming increases, so do drownings).

When a correlation exists between variables, what is implied is that a prediction can be made but causation cannot be shown. Some examples in a behavioral science setting where correlation studies are used could be studying the relationship between gender and jury selection, the relationship between age and verdict delivery, or the use of an aptitude test to predict success on a job. Correlations can be one of the three types: positive, negative, or inverse. A positive correlation is when one variable increases, so does the other. A negative correlation is when one variable decreases, so does the other. An inverse correlation is when one variable increases, the other decreases.

Causal-comparative or quasi-experimental research generally attempts to establish a cause–effect connection between and among the variables being studied. Basically what is being established is what effect the independent variable or variables (treatment variables) have on the dependent or the measurement (outcome) variable. The independent variables are usually the groups of the study (gender, socioeconomic class, or length of service) or some specific treatment provided to the group. All data collected is numeric, and assignment of the participants to each group is random or nonrandom.

Some examples of causal-comparative research in the behavioral science environment are studying the effects of diversity training on employee turnover, the effects of overtime hours on anxiety levels, or even the effects of increased alcohol consumption on mental illness.

Experimental research, like causal-comparative or quasi-experimental research, attempts to establish the effects of the independent variable on the dependent or outcome variable. The difference is that experimental research is conducted in highly controlled environments and generally dedicated to situations or problems requiring scientific exactness.

Examples of experimental research in behavioral sciences might be studying the effects of a new drug on Alzheimer's disease, testing the effects of increased tensile strength on a new child's toy, or testing the effects of a new cosmetic on allergic skin reactions.

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