Predictive Statistics in Counseling (Barrio Minton & Lenz, 2026, Ch 5-4C-B)

Predictive Statistics in Counseling (Barrio Minton & Lenz, 2026, Ch 5-4C-B) I. Introduction to Predictive Value This video covers chapter five sub-section four C of the text. We introduce predictive statistics in clinical counseling. The content aligns with CACREP program evaluation standards. We study these analytics through a contemplative counseling lens. Mindful observation of predictive models fosters right view. II. Understanding Simple Linear Regression Simple linear regression uses one predictor variable. This predictor is an interval-ratio independent variable. It is used to predict scores on an interval-ratio outcome. An example is using GPA to predict school disciplinary actions. This represents a simple model of cause and effect in data. We observe these simple predictions with beginner's mind. III. Multiple Regression and Model Types Multiple linear regression uses two or more predictor variables. Predictors can combine to forecast continuous clinical outcomes. Simultaneous regression enters all predictors at the same time. Hierarchical regression enters variables in intentional blocks. This shows if adding new variables improves our prediction. We observe these complex networks with deep clinical clarity. IV. Logistic Regression for Categorical Outcomes Logistic regression predicts a nominal outcome variable. The dependent variable typically has two distinct levels. Predictors can determine referral to a specific group program. This helps us see which factors influence treatment placement. Ethical prediction ensures we support clients with skill. We use these models to allocate counseling resources. V. Preparing Clients and Supporting Expectations Understanding predictive tests helps us make clinical predictions. We can forecast outcomes for individual clients using data. This enables us to prepare clients and support expectations. Client preparedness impacts psychedelic-assisted therapy outcomes. Client expectation also predicts therapeutic experiences. Sharing this predictive data can enhance client motivation. VI. Identifying and Impacting Predictive Variables Predictive tests help us find variables we can actively impact. In binge eating disorder stress hormone levels predict episodes. Cortisol levels have been found to precede binge behaviors. This suggests clear benefits for stress management therapies. Mindfulness-based stress reduction can help regulate cortisol. Behavioral health coaching can target these predictive markers. VII. Clinical Focus in Binge Eating Disorder Early behavioral and emotional change predicts long-term outcomes. Change in the first two to three weeks is highly predictive. Clients who change early tend to do very well long-term. Clients who do not change early rarely see changes later. We should focus on behavior change in those first sessions. We maximize client motivation rather than focusing on readiness. VIII. Contemplative and Practical Clinical Interventions If clients lack resources early changes are statistically unlikely. Food insecurity and poor social support hinder behavior changes. Therapists must prioritize enabling factors for these clients. We provide resources for food security such as food pantries. We connect clients with free support groups like OA. Alternatively we can discuss this predictive data with clients. IX. Additional Resource Support See NourishED RFI's NotebookLM Resource Support Page. https://notebooklm.google.com/noteboo... X. Source Barrio Minton, C. A., & Lenz, A. S. (2026). Chapter 5: Measurements & Statistics. In Practical Approaches to Applied Research and Program Evaluation for Counselors (2nd ed.). XI. AI Generation and Review Disclaimer This content was generated by AI (NotebookLM). Content generation was guided & reviewed by Brenna Bray, PhD. Dr. Bray holds a PhD in Biomedical Science & Neuroscience. She has postdoctorate training in Eating Disorders & Integrative Health. Her training was NIH-funded (‪@NIHgov‬ ‪@NIDANIH‬ ‪@NIH_NCCIH‬ ). Dr. Bray serves as a researcher; university professor; health, nutrition, & behavior change coach; & Founder, Director, and CEO of the NourishED Research Foundation (https://nourishedrfi.org). #counselingresearch #PredictiveStatistics #regressionanalysis #linearregression #logisticregression ⁨‪@buddhistcounsellingandpsyc15‬ ‪@TheAPAVideo‬ ‪@AmericanPsychiatricAssociation‬ ‪@talkscounseling‬ ‪@researchmethodsandstatisti2134‬ ‪@NCRMUK‬