Basic Research Methods in Psychology

This chapter begins with an explanation of why Psychology majors (who typically have an aversion to mathematics) need to develop a solid understanding of basic statistics. We then move into a discussion of basic research methodology, describing independent variables (including the treatment and control groups), dependent variables, and confounding variables. These concepts are illustrated using an example of testing the effect of statistics tutoring by comparing a group of tutored students (treatment group) to a group of students who are not tutored (control group); potential confounds such as prior knowledge of statistics are discussed. A second example, eating chocolate with the goal of reducing stress, is used to review these concepts and guide students through brainstorming potential confounds. Next, we present the four levels of measurement: nominal, ordinal, interval, and ratio. These levels are categorized as either categorical or continuous, and multiple real-world examples are provided for each level. We briefly discuss Cozby’s (Methods in Behavioral Statistics (9th ed.). McGraw-Hill, 2006) goals of science and use that context to define descriptive versus inferential statistics. We introduce the concepts of reliability and validity to students, as they are essential concepts to understand when one is evaluating the findings of any scientific study. We emphasize that error in science is inevitable but is something to be minimized to the extent possible. We illustrate the concepts of reliability and validity with the example of taking one’s temperature with a thermometer, yielding good, consistent measurement (98.6 degrees on each of multiple measurements), bad but consistent measurement (57.2 degrees on each of multiple measurements), and bad inconsistent measurement (temperature readings that are all over the place). The chapter moves to discussions of how to organize and illustrate data, discussing frequency diagrams (frequency distribution tables, histograms, and frequency polygons) and shapes of distributions (including skew and kurtosis), and provides illustrations of each diagram and distribution shape, conforming to APA’s seventh edition standards. The chapter concludes with a brief summary of important concepts and step-by-step instructions on how to install Microsoft Excel’s Data Analysis Toolpak for Windows and MacOS. Finally, critical-thinking questions and practice problems are provided. Information boxes provided in this chapter elaborate on the reason why we avoid using the word “prove” in science, provide additional examples of levels of measurement, introduce the Central Limit Theorem, and provide brief biographies of Stanley Smith Stevens, who first described the four levels of measurement, and Florence Nightingale, whose Polar Pie Chart is a round version of a histogram.