Quantitative Research & Statistics in Counseling

PREREQUISITE: All required coursework must be completed before enrolling in this course.

This course on quantitative research in counseling offers an emphasis on applied statistical strategies for analyzing data while setting the foundations for the Applied Project course. Students use open‑source tools (jamovi and JASP) to design and execute reproducible analyses emphasizing research design, effect sizes, power, the GLM (ANOVA/Regression), generalized linear models, multilevel models, and psychometrics (reliability/validity; CFA; measurement invariance), as well as missing data and meta‑analytic reasoning. Ethical statistical practice, APA JARS compliance, open Science, and culturally responsive methodology are embedded. The course helps students begin to conceptualize their Applied Doctoral Project Proposal assignment for execution in the Applied Project II course.

UPON COMPLETION OF THE COURSE, THE STUDENT WILL BE COMPETENT IN:

• Appraising quantitative designs in counseling and aligning questions, designs, and analysis.
• Implementing reproducible, open‑source analyses (Jamovi/JASP) with ethical data management and documentation consistent with JARS.
• Computing and interpreting effect sizes and power; justify sample size decisions for the planned project.
• Interpreting GLM models (t/AN(C)OVA; multiple regression) and GLMs (logistic/Poisson).
• Conducting multilevel modeling for nested data, when appropriate to the selected dataset.
• Evaluating measurement quality (reliability; construct validity); conduct CFA and evaluate measurement invariance across groups.
• Addressing missing data (MCAR/MAR/MNAR) with modern remedies (e.g., multiple imputation) and sensitivity analyses.
• Synthesizing evidence using meta‑analytic reasoning; critically appraise meta‑analyses relevant to the project.
• Producing an Applied Project Proposal Pack: topic justification, literature map, preregistration, analysis plan, dataset/codebook, and pilot diagnostics—ready for execution in the Applied Project course.
• Incorporating AI-technologies and the use of tools transparently, thoughtfully, and judiciously (literature mapping, code/table checks) with transparent attribution and human verification.
• Appraising quantitative designs in counseling and aligning questions, designs and analyses.
• Implementing reproducible, open‑source analyses (jamovi/JASP) with ethical data management and documentation consistent with JARS.
• Computing and interpreting effect sizes and power; justify sample size decisions for the planned project.
• Interpreting GLM models (t/AN(C)OVA; multiple regression) and GLMs (logistic/Poisson).
• Conducting multilevel modeling for nested data, when appropriate to the selected dataset.
• Evaluating measurement quality (reliability; construct validity); conduct CFA and evaluate measurement invariance across groups.
• Addressing missing data (MCAR/MAR/MNAR) with modern remedies (e.g., multiple imputations) and sensitivity analyses.
• Synthesizing evidence using meta‑analytic reasoning; critically appraise meta‑analyses relevant to the project.
• Producing an Applied Project Proposal Pack: topic justification, literature map, preregistration, analysis plan, dataset/codebook, and pilot diagnostics—ready for execution in the Applied Project course.
• Incorporation of AI-technologies and use of tools transparently, thoughtfully, and judiciously (literature mapping, code/table checks) with transparent attribution and human verification.

ACQUIRED SKILLS:

• Reproducible, license-free workflows in open source online software jamovi/JASP
• Effect size computation and power analysis for planned models
• GLM/GLZ modeling with assumption checks and diagnostics
• Multilevel modeling for nested counseling data (when dataset permits)
• Psychometric evaluation including CFA and measurement invariance
• Modern handling of missing data (e.g., multiple imputation) and sensitivity analyses
• Transparent reporting aligned with APA JARS and ethical statistical practice
• Project scoping, preregistration, and dataset vetting for an Applied Project