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Statistical Analysis in Psychological Research

via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

6 weeks, 2 hours a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Found in

Overview

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Gain a thorough understanding of statistical techniques used in psychology, from descriptive statistics to advanced inferential methods. This course equips learners with the skills to analyze, interpret, and draw meaningful conclusions from psychological data. Focusing on the quantitative side of psychological research, this course covers descriptive statistics, principles of statistical inference, and key statistical tests such as t-tests, correlation, ANOVA, and non-parametric methods. Learners will develop the ability to summarize data, test hypotheses, and interpret statistical results in the context of psychological studies. The course emphasizes both conceptual understanding and practical application, preparing learners to critically assess and conduct statistical analyses. Through a structured progression of readings, videos, and quizzes, the course builds statistical literacy and confidence. Learners are supported in developing a deep understanding of statistical reasoning and its application to real-world psychological research. This course is part two of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization. This Specialization is based on the book Research Methods and Statistics in Psychology, by S Alexander Haslam and Craig McGarty. Copyright ©2019 by Sage Publications Limited. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies. Published by Sage Publications Limited, London. Used by arrangement with Sage Publications Limited.

Syllabus

Descriptive Statistics

This module introduces key concepts in summarizing and interpreting psychological data using descriptive statistics. Learners will explore measures of central tendency and dispersion, understand the distinction between samples and populations, and learn how to accurately describe and compare data sets for research purposes.

Some Principles of Statistical Inference

This module introduces the concepts of descriptive and inferential uncertainty in psychological research, guiding learners through probability-based inference and its application to both individual and group data. Learners will gain practical tools for evaluating research findings and making confident, evidence-based conclusions.

Examining Differences between Means: The t-test

This module introduces the t-test as a statistical tool for comparing means when population parameters are unknown. Learners will explore different types of t-tests, interpret results using probability and confidence intervals, and understand the assumptions and controversies surrounding hypothesis testing. Practical examples and alternative approaches to inference are also discussed.

Examining Relationships between Variables: Correlation

This module introduces the concept of correlation, guiding learners through the process of quantifying and interpreting relationships between variables using Pearson's r. Participants will learn to visualize bivariate data, calculate correlation coefficients, and recognize the limitations and assumptions of correlational analysis. Practical examples and cautions help ensure accurate interpretation and application of results.

Comparing Two or More Means by Analysing Variances: ANOVA

This module introduces the principles and applications of analysis of variance (ANOVA) for comparing means across multiple groups. Learners will explore the rationale for analyzing variances, understand the calculation and interpretation of F-ratios, and examine significance testing and effect sizes in research contexts. The module also covers both one-way and two-way ANOVA, as well as practical considerations for conducting and evaluating ANOVA in experimental studies.

Analysing Other Forms of Data: Chi-square and Distribution-free Tests

This module introduces statistical methods for analyzing categorical and non-normally distributed data, including chi-square and distribution-free tests. Learners will discover how to select and apply appropriate tests based on data characteristics and research questions. Practical examples and checklists help reinforce the decision-making process for real-world data analysis.