Data Analysis for the Behavioral Sciences

This ten-hour course introduces the fundamental concepts of statistical analysis, providing a comprehensive understanding of how to handle, interpret, and present data. The course begins with an introduction to the nature of data and variables, in which students will learn different ways to categorize variables, including according to scale of measurement. Measures of central tendency, measures of dispersion, measures of correlation, and basic data-visualization methods are introduced to show how researchers summarize their data sets. The understanding and interpretation of these statistics is emphasized, with focus, for example, on why correlation does not imply causation, but causation implies correlation.

After consideration of descriptive statistics, the course moves into the logic of inferential statistics, which are used to make predictions or inferences about a population based on a sample. This section includes understanding sampling distributions and the central limit theorem, and sets the stage for consideration of null hypothesis significance testing (NHST). Students will learn about p-values, significance levels, and the steps involved in hypothesis testing.

After consideration of some of the concerns about NHST methods, the course considers how deeper consideration of the NHST approach, such as through power analysis and the use of confidence intervals, or alternative methods such as meta-analysis, model building, and Bayesian statistics, might be used instead of the bare-bones NHST approach.

The emphasis in the course is on conceptual understanding rather than calculation, so based on study criteria, students will learn to select the appropriate inferential test, such as t-tests, chi-square tests, ANOVA, and regression analysis. By the end of the course, students will have a solid foundation in statistical methods, enabling them to analyze data critically and apply statistical techniques confidently in their research endeavors.

Learning objectives

• Explain various ways to categorize variables.
• Explain various ways to describe data.
• Describe how graphs are used to visualize data.
• Explain the meaning of a correlation coefficient.
• Describe the logic of inferential statistics.
• Explain the logic of null hypothesis significance testing.
• Select the appropriate inferential test based on study criteria.
• Compare and contrast the use of statistical significance, effect size, and confidence intervals.
• Explain the importance of statistical power.
• Describe how alternative procedures address the major objections to null hypothesis significance testing.