Design and Analysis in Educational Research Using jamovi: ANOVA Designs

✍ Scribed by Kamden K. Strunk, Mwarumba Mwavita

• Publisher: Routledge
• Year: 2021
• Tongue: English
• Leaves: 302
• Edition: 1
• Category: Library

✦ Synopsis

Design and Analysis in Educational Research Using jamovi is an integrated approach to learning about research design alongside statistical analysis concepts. Strunk and Mwavita maintain a focus on applied educational research throughout the text, with practical tips and advice on how to do high-quality quantitative research.

Based on their successful SPSS version of the book, the authors focus on using jamovi in this version due to its accessibility as open source software, and ease of use. The book teaches research design (including epistemology, research ethics, forming research questions, quantitative design, sampling methodologies, and design assumptions) and introductory statistical concepts (including descriptive statistics, probability theory, sampling distributions), basic statistical tests (like z and t), and ANOVA designs, including more advanced designs like the factorial ANOVA and mixed ANOVA.

This textbook is tailor-made for first-level doctoral courses in research design and analysis. It will also be of interest to graduate students in education and educational research. The book includes Support Material with downloadable data sets, and new case study material from the authors for teaching on race, racism, and Black Lives Matter, available at www.routledge.com/9780367723088.

✦ Table of Contents

Cover
Half Title
Endorsement Page
Title Page
Copyright Page
Table of Contents
Acknowledgments
Part I: Basic issues
Chapter 1: Basic issues in quantitative educational research
Research problems and questions
Finding and defining a research problem
Broad
Meaningful
Theoretically driven
Defining and narrowing research questions
Answerable
Specific and operationally defined
Meaningful
Reviewing the literature relevant to a research question
Finding published research
Reading published research and finding gaps
Types of research methods
Epistemologies, theoretical perspectives, and research methods
Epistemology and the nature of knowledge
Positivism
Post-positivism
Interpretivism
Critical approaches
Deconstructivism
Connecting epistemologies to perspectives and methods
Overview of ethical issues in human research
Historical considerations
The Belmont Report
Respect for persons
Beneficence
Justice
The common federal rule
Informed consent
Explanation of risks
Deception
Benefits and compensation
Confidentiality and anonymity
Institutional Review Board processes
Conclusion
Chapter 2: Sampling and basic issues in research design
Sampling issues: populations and samples
Sampling strategies
Random sampling
Representative (quota) sampling
Snowball sampling
Purposive sampling
Convenience sampling
Sampling bias
Self-selection bias
Exclusion bias
Attrition bias
Generalizability and sampling adequacy
Levels of measurement
Nominal
Ordinal
Interval
Ratio
A special case: Likert-type scales
Basic issues in research design
Operational definitions
Random assignment
Experimental vs. correlational research
Basic measurement concepts
Score reliability
Test–retest reliability
Alternate forms reliability
Internal consistency reliability
Validity of the interpretation and use of test scores
Content validity
Criterion validity
Structural validity
Construct validity
Finding and using strong scales and tests
Conclusion
Chapter 3: Basic educational statistics
Central tendency
Mean
Median
Mode
Comparing mean, median, and mode
Variability
Range
Variance
Standard deviation
Interpreting standard deviation
Visual displays of data
The normal distribution
Skew
Kurtosis
Other tests of normality
Standard scores
Calculating z -scores
Calculating percentiles from z
Calculating central tendency, variability, and normality estimates in jamovi
Conclusion
Note
Part II: Null hypothesis significance testing
Chapter 4: Introducing the null hypothesis significance test
Variables
Independent variables
Dependent variables
Confounding variables
Hypotheses
The null hypothesis
The alternative hypothesis
Overview of probability theory
Calculating individual probabilities
Probabilities of discrete events
Probability distributions
The sampling distribution
Calculating the sampling distribution
Central limit theorem and sampling distributions
Null hypothesis significance testing
Understanding the logic of NHST
Type I error
Type II error
Limitations of NHST
Looking ahead at one-sample tests
Notes
Chapter 5: Comparing a single sample to the population using the one-sample Z -test and one-sample t -test
The one-sample Z -test
Introducing the one-sample Z -test
Design considerations
Assumptions of the test
Calculating the test statistic
Calculating and interpreting effect size estimates
Interpreting the pattern of results
The one-sample t -test
Introducing the one-sample t -test
Design considerations
Assumptions of the test
Calculating the test statistic
Calculating and interpreting effect size
Interpreting the pattern of results
Conclusion
Part III: Between-subjects designs
Chapter 6: Comparing two sample means: The independent samples t -test
Introducing the independent samples t -test
The t distribution
Research design and the independent samples t -test
Assumptions of the independent samples t -test
Level of measurement for the dependent variable is interval or ratio
Normality of the dependent variable
Observations are independent
Random sampling and assignment
Homogeneity of variance
Levene’s test
Correcting for heterogeneous variance
Calculating the test statistic t
Calculating the independent samples t -test
Partitioning variance
Between-groups and within-groups variance
Using the t critical value table
One-tailed and two-tailed t -tests
Interpreting the test statistic
Effect size for the independent samples t -test
Calculating Cohen’s d
Calculating ω2
Interpreting the magnitude of difference
Determining how groups differ from one another and interpreting the pattern of group differences
Computing the test in jamovi
Writing Up the Results
Notes
Chapter 7: Independent samples t -test case studies
Case study 1: written versus oral explanations
Research questions
Hypotheses
Variables being measured
Conducting the analysis
Write-up
Case study 2: evaluation of implicit bias in graduate school applications
Research questions
Hypotheses
Variables being measured
Conducting the analysis
Write-up
Note
Chapter 8: Comparing more than two sample means: The one-way ANOVA
Introducing the one-way ANOVA
The F distribution
Familywise error and corrections
Why not use multiple t -tests?
Familywise error
The Bonferroni correction
Omnibus tests and familywise error
Research design and the one-way ANOVA
Assumptions of the one-way ANOVA
Level of measurement for the dependent variable is interval or ratio
Normality of the dependent variable
Observations are independent
Random sampling and assignment
Homogeneity of variance
Levene’s test for equality of error variances
Correcting for heterogeneous variances
Calculating the Test Statistic F
Calculating the one-way ANOVA
Partitioning variance
Between-groups and within-groups variance
Completing the source table
Using the F critical value table
F is always a one-tailed test
Interpreting the test statistic
Effect size for the one-way ANOVA
Calculating omega squared
Interpreting the magnitude of difference
Determining how groups differ from one another and interpreting the pattern of group differences
Post-hoc tests
Calculating Tukey’s HSD
Comparison of available Post-hoc tests
Making sense of a pattern of results on the post-hoc tests
A priori comparisons
Introducing planned contrasts
Setting coefficients for orthogonal contrasts
Calculating planned contrasts in the ANOVA model
Interpreting planned contrast results
Computing the one-way ANOVA in jamovi
Computing the one-way ANOVA with post-hoc tests in jamovi
Computing the one-way ANOVA with a priori comparisons in jamovi
Writing Up the Results
Writing the one-way ANOVA with post-hoc tests
Chapter 9: One-way ANOVA case studies
Case study 1: first-generation students’ academic success
Research questions
Hypotheses
Variables being measured
Conducting the analysis
Write-up
Case study 2: income and high-stakes testing
Research questions
Hypotheses
Variables being measured
Conducting the analysis
Write-up
Notes
Chapter 10: Comparing means across two independent variables: The factorial ANOVA
Introducing the factorial ANOVA
Interactions in the factorial ANOVA
Research design and the factorial ANOVA
Assumptions of the factorial ANOVA
Level of measurement for the dependent variable is interval or ratio
Normality of the dependent variable
Observations are independent
Random sampling and assignment
Homogeneity of variance
Levene’s test for equality of error variances
Calculating the test statistic F
Calculating the factorial ANOVA
Partitioning variance
Calculating the factorial ANOVA source table
Using the F critical value table
Interpreting the test statistics
Effect size for the factorial ANOVA
Calculating omega squared
Computing the test in jamovi
Computing the factorial ANOVA in jamovi
Determining how cells differ from one another and interpreting the pattern of cell differences
Simple effects analysis for significant interactions
Calculating the simple effects analysis in jamovi
Interpreting the pattern of differences in simple effects analysis
Interpreting the main effects for nonsignificant interactions
Writing up the results
Note
Chapter 11: Factorial ANOVA case studies
Case Study 1: bullying and LGBTQ youth
Research questions
Hypotheses
Variables being measured
Conducting the analysis
Write-up
Case study 2: social participation and special educational needs
Research questions
Hypotheses
Variables being measured
Conducting the analysis
Write-up
Note
Part IV: Within-subjects designs
Chapter 12: Comparing two within-subjects scores using the paired samples t -test
Introducing the paired samples t -Test
Research design and the paired samples t -Test
Assumptions of the paired samples t -Test
Level of measurement for the dependent variable is interval or ratio
Normality of the dependent variable
Observations are independent
Random sampling and assignment
Calculating the test statistic t
Calculating the paired samples t -test
Partitioning variance
Using the t critical value table
One-tailed and two-tailed t -tests
Interpreting the test statistics
Effect size for the paired samples t -test
Determining and interpreting the pattern of difference
Computing the test in jamovi
Writing up the results
Chapter 13: Paired samples t -test case studies
Case study 1: guided inquiry in chemistry education
Research questions
Hypotheses
Variables being measured
Conducting the analysis
Write-up
Case study 2: student learning in social statistics
Research questions
Hypotheses
Variables being measured
Conducting the analysis
Write-up
Notes
Chapter 14: Comparing more than two points from within the same sample: The within-subjects ANOVA
Introducing the within-subjects ANOVA
Research design and the within-subjects ANOVA
Assumptions of the within-subjects ANOVA
Level of measurement for the dependent variable is interval or ratio
Normality of the dependent variable
Observations are independent
Random sampling and assignment
Sphericity
Mauchly’s test for sphericity
Corrections for lack of sphericity
Calculating the test statistic F
Partitioning variance
Between, within, and subject variance
Completing the source table
Using the F critical value table
Interpreting the test statistic
Effect size for the within-subjects ANOVA
Calculating omega squared
Eta squared
Determining how within-subjects levels differ from one another and interpreting the pattern of differences
Comparison of available pairwise comparisons
Interpreting the pattern of pairwise differences
Computing the within-subjects ANOVA in jamovi
Writing Up the Results
Chapter 15: Within-subjects ANOVA case studies
Case study 1: mindfulness and psychological distress
Research questions
Hypotheses
Variables being measured
Conducting the analysis
Write-up
Case study 2: peer mentors in introductory courses
Research questions
Hypotheses
Variables being measured
Conducting the analysis
Write-up
Notes
Chapter 16: Mixed between- and within-subjects designs using the mixed ANOVA
Introducing the mixed ANOVA
Research design and the mixed ANOVA
Interactions in the mixed ANOVA
Assumptions of the mixed ANOVA
Level of measurement for the dependent variable is interval or ratio
Normality of the dependent variable
Observations are independent
Random sampling and assignment
Homogeneity of variance
Sphericity
Calculating the test statistic F
Effect size in the mixed ANOVA using ETA squared
Computing the mixed ANOVA in jamovi
Determining how cells differ from one another and interpreting the pattern of cell difference
Post-hoc tests for significant interactions
Writing up the results
Notes
Chapter 17: Mixed ANOVA case studies
Case study 1: implicit prejudice about transgender individuals
Research questions
Hypotheses
Variables being measured
Conducting the analysis
Write-up
Case study 2: suicide prevention evaluation
Research questions
Hypotheses
Variables being measured
Conducting the analysis
Write-up
Note
Part V: Considering equity in quantitative research
Chapter 18: Quantitative methods for social justice and equity: Theoretical and practical considerations 1
Quantitative methods are neither neutral nor objective
Quantitative Methods and the Cultural Hegemony of Positivism
Dehumanization and reimagination in quantitative methods
Practical considerations for quantitative methods
Measurement issues and demographic data
Other practical considerations
Possibilities for equitable quantitative research
Choosing demographic items for gender and sexual identity
Note
Appendices
B1 statistical notation and formulas
Descriptive statistics and standard scores
Probabilities
One-sample tests
Independent samples t -test
One-way ANOVA
Factorial ANOVA
Paired samples t -test
Within-subjects ANOVA
References
Index