Overview
INTRODUCTION:
First, we will explore categorical analyses, focusing on two essential statistical tests: the chi-square goodness-of-fit test and the chi-square test of independence.
- Chi-square goodness-of-fit test: This test determines whether the observed frequencies of a single categorical variable differ significantly from expected frequencies. It helps us understand if a sample comes from a specific distribution. For example, a psychologist might investigate whether the distribution of personality types (e.g., introverted, extroverted, and ambiverted) among a sample of students from one university matches the expected distribution based on national statistics.
- Chi-square test of independence: This test assesses whether two categorical variables are related to each other. For instance, a psychologist might examine whether there is an association between the level of stress (categorized as low, moderate, and high) and the type of coping strategy an individual tends to use (such as problem-focused, emotion-focused, and avoidance). By applying the chi-square test of independence, researchers can determine if the observed relationship is statistically significant or if it occurred by chance.
Next, we will cover ANOVA. If you read a lot of quantitative research studies, you will encounter various types of ANOVA. We will start by exploring the basic logic behind ANOVA and how it works. ANOVA helps us test hypotheses about group differences by partitioning the total variability in the data into components attributable to different sources. You will learn how to run an ANOVA in JASP and interpret the output, which is similar to the output produced by other statistical software and the formatting seen in published research articles. You will also learn about post-hoc tests, conducted after finding a significant ANOVA result to identify which specific groups differ from each other, and the assumptions of ANOVA. At the end of the chapter, more advanced forms of ANOVA will be introduced, including repeated measures ANOVA, factorial ANOVA, analysis of covariance (ANCOVA), and multivariate analysis of variance (MANOVA).
OVERVIEW:
For this assessment you will conduct and interpret a chi-square test. You will also read a published research study that uses some form of an analysis of variance (ANOVA) and you will examine a graduate program or job posting in the social sciences that uses statistics.
PREPARATION:
Before you begin this assessment, complete the following:
- Download the Assessment 5 Worksheet [DOCX].
- Download the Assessment_5_Data.csv file.
- For Part 2a, locate one of the articles listed in the assessment in the Capella University Library. See Finding Articles Using the Journals Tab [Video] for additional instructions.
- For Part 2b, locate a graduate program or job posting in the social sciences that uses statistics. This should be different from the program or career you used in the Assessment 1.
SOURCES:
Read the following from Learning Statistics with JASP: Capella PSYC3700 Version.
- Assessment 5: Categorical Data Analysis and Analysis of Variance reading list.
- Chapter 12, "Categorical Data Analysis.”
- Chapter 13, ”Analysis of Variance.”
These videos demonstrate how to obtain the output shown in the textbook.
- JASP – Frequency Table: Randomness.csv.
- JASP – Chi-Square Goodness-of-Fit Test: Randomness.csv, Equal Proportions.
- JASP – Chi-Square Goodness-of-Fit Test: Randomness.csv, Different Proportions.
- JASP - Contingency Table: Chapek9.
- JASP - Chi-Square Test of Independence: Chapek9.
- JASP – Descriptive Statistics & Side-by-Side Boxplots by Group.
- JASP - One-Way Between Groups ANOVA: Clinical Trial Example.
The following videos provide more information related to particular topics in the readings.
- Chi-Square Goodness-of-Fit Test: Hand Calculations.
- Chi-Square Test of Independence: Hand Calculations.
- Chi-Square Test of Independence: Effect Size.
GRADING COMPETENCIES:
By successfully completing this assessment, you will demonstrate your proficiency in the following course competencies and scoring guide criteria:
- Competency 2: Interpret statistical findings in the context of their level of statistical and practical significance.
- Interpret the statistical significance of a chi-square test.
- Identify the descriptive and inferential analyses conducted in the article.
- Identify the type of analysis of variance conducted in the study.
- Identify the variables in the analysis of variance conducted in the study.
- Summarize the results of an analysis of variance.
- Competency 3: Conduct statistical analyses to address research questions in the social sciences.
- Conduct a chi-square test in JASP.
- Explain how to check the assumptions of a chi-square test.
- Competency 4: Determine the generalizability of specific findings based on parameters of the research design.
- Identify the sample used in the study and the population to which the results of the study can be generalized.
- Competency 5: Evaluate academic options and careers in the social sciences that use statistics.
- Describe a graduate program or career in the social sciences that uses statistics.
- Explain why you think you would or would not be interested in pursuing the program or career you selected.
- Competency 7: Explain the results of statistical analyses in common language.
- Write a paragraph in APA style summarizing the results of your analysis.