Statistics in Psychological Research

Unlock the power of data with this 10-hour, comprehensive course in data analysis. This course is perfect for anyone looking to deepen their knowledge and apply statistical methods effectively in psychology or related fields.

The course begins with consideration of how researchers define and categorize variables, including the nature of various scales of measurement and how these classifications impact data analysis and interpretation. This is followed by a thorough introduction to the measures of central tendency, variability, and correlation that researchers use to describe their findings, providing an understanding of such topics as which descriptive statistics are appropriate for given research designs, the meaning of a correlation coefficient, and how graphs are used to visualize data.

The course then moves on to a conceptual treatment of foundational inferential statistics that researchers use to make predictions or inferences about a population based on a sample. The focus is on understanding the logic of these statistics, rather than on making calculations. Specifically, the course explores the logic behind null hypothesis significance testing, long a cornerstone of statistical analysis. Learn how to formulate and test hypotheses and understand the significance of p-values in determining the validity of your results. The course reviews how to select the appropriate inferential test based on your study criteria. Whether it’s t-tests, ANOVA, chi-square tests, or regression analysis, you’ll know which test to apply and when.

In keeping with growing concerns about some of the limitations of null hypothesis significance testing, such as its role in the so-called replication crisis, the course also delves into these concerns and possible ways to address them, including introductory consideration of statistical power and alternatives to hypothesis testing like estimation techniques and confidence intervals, meta-analysis, modeling, and Bayesian inference.

Learning objectives

• Explain various ways to categorize variables.
• 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.
• Explain various ways to describe data.
• Describe how graphs are used to visualize data.
• Explain the meaning of a correlation coefficient.