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LIVE STREAM – 4-day Statistics Psychometric Training Online Course

Seminar Overview:

This introductory 4-day psychometric training online course will teach you to be proficient in the application of psychometrics. Participants should be proficient specific to the material covered in a two-semester graduate-level social science statistics course sequence.

Seminar Topics:

  • Measurement and statistical concepts specific to psychometric training
  • Scaling, scaling models & scale development – stimulus, response and subject centered
  • Validity – conceptual and statistical aspects necessary for evidential arguments
  • Introduction to Factor analysis – traditional, IRT and SEM-based approaches/connections
  • Reliability – classical and modern approaches to estimation of score reliability
  • Introduction to Item Response Theory

Psychometric Training Description:

Psychometrics is defined as the science of evaluating the characteristics of tests or other devices designed to measure psychological attributes of people. Tests are broadly defined as devices for measuring ability, aptitude, achievement, attitudes, interests, personality, cognitive functioning, and mental health. Application of psychometrics to psychology and social/behavioral science constitutes an organized effort to (a) properly use theory-based measurement procedures for the development of tests and other measurement instruments for inter- and intraindividual research designs and (b) incorporate current best practices for applying measurement theory, item/scale development, reliability estimation (classical and modern), factor analysis/IRT and establishing statistical evidence of score validity through a unified approach. advance knowledge in psychological and sensory processes. Participants will receive an electronic copy of all course materials, including lecture slides, practice datasets, software scripts, relevant supporting documentation, and recommended readings. Participants will also have access to a video recording of the course.

Instructor: Larry Price, Ph.D.

psychometric training online

Larry Price, Ph.d. is a Professor of Psychometrics & Statistics and Director of the Office of Data Analytics & Methodology at Texas State University. Between 1999 and 2002, Dr. Price was employed at The Psychological Corporation in San Antonio as a Senior Psychometrician/Statistician where his work focused on improving the psychometric properties of the Wechsler Scales of Intelligence Memory (e.g., WISC-III, WISC-IV, WAIS-III, WMS-III, and WPPSI-III), and Achievement (WIAT-II) and other psychological measures such as the Beck Depression Inventory (BDI) and Clinical Evaluation of Language Fundamentals (CELF-IV). His research interest includes the theoretical development and testing of Bayesian and non-Bayesian psychometric models in psychological and neuropsychological research (neuroimaging network analysis), theoretical development, testing, and refinement of classical and modern psychometric methods in the behavioral sciences, development of dynamic multivariate time series models for the psychological, social and neurosciences. Prior to working at Psychological Corporation, he worked at Emory University from 1986 to 1999 as a Biostatistician and Psychometrician in the School of Medicine. Funding mechanisms for Dr. Price’s work include NIH, NSF, DOE, and private organizations.

APA Continuing Education Credits:

psychometric training online course

Please contact us for exact # of credit hours for continuing education credits. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content.

Psychometric Training Online Includes:

Materials, downloads, recorded course video viewable for up to one year.

Learning Objectives:

  • Acquire a basic understanding of the role of psychometrics as applied to social and behavioral sciences.
  • Develop a clear understanding of the conceptual and theoretical basis of measurement and statistical concepts specific to psychometrics.
  • Acquire knowledge of how to properly apply psychometric techniques such as scale development, item analysis/refinement, score reliability and statistical validity.
  • Gain knowledge of how to apply factor analysis using traditional and structural equation modeling approaches.
  • Gain knowledge of how to apply generalizability theory for estimating variance components and score reliability when classical test theory model is inadequate.
  • Acquire basic knowledge of how and why to apply item response theory for scaling test data.

Seminar Prerequisites:

Required:

  • Intermediate proficiency in basic statistical theory as would be gained in a 1st year graduate course.

Not required but advantageous:

  • Limited experience (e.g., graduate-level course) with classical measurement theory and concepts.

No level of proficiency beyond basic awareness is assumed for skills related to:

  • Advanced mathematical or statistical topics such as matrix algebra.

Software and Computer Support:

Participants need to bring a laptop computer with Wi-Fi capabilities. Students should have access to IBM SPSS, version 21.0 or higher and Mplus, version 7.1 or higher and R.

All statistical software used at Stats Camp will be available, free to participants, on our SMORS (statistical modeling on remote servers) system for the duration of camp.

 

Day 1  
9:00-9:30 Welcome and introductions
9:30-10:30 Network science and psychometrics – definition, data generation processes, relationship to classical & modern psychometrics, graphical models, empirical/theoretical network systems, association and causation, machine learning techniques and algorithms
10:30-11:30 Introduction to R Studio and BayesiaLab software
11:30-12:30 Lunch break
12:30-1:30 Computer exercises in R – creating network models
1:30-3:00 Defining and interpreting structure and quality of network models, unsupervised and supervised learning in graphical models, undirected and directed graphical models, interpretation of network descriptive measures, software considerations
Day 2
9:00-10:00 Exercise on graph construction, independence, conditional dependence in graph theory, model selection, workflow in conducting network models
10:00-11:30 Introduction to Pairwise Markov Random Fields (PMRFs): Graphical Models for Binary Data (GMBDs); Gaussian Graphical Models (GGMs) for continuous data; Mixed Graphical Models (MGMs) for continuous, categorical and count data
11:30-12:30 Lunch break
12:30-1:30 Computer exercises – IGMs, GGMs, MGMs
1:30-3:30 Multiple group network modeling – single sample and multiple sample stability and replication
Day 3  
9:00-10:30 Probabilistic structural equation modeling (pSEM) for factor and principal component analysis. Comparison to traditional approaches to PCA, FA, & SEM
10:30-11:30 Computer exercises – creating and interpreting pSEMs
11:00-12:00 Graphical Models for Binary Data and relationship to Item Response Theory
12:00-1:00 Lunch break
1:00-3:00 Longitudinal network analysis – design and problem selection/description, contemporaneous and temporal measurement, vector auto-regression, model estimation and interpretation, group-level and single-subject modeling
3:15-5:00 * Individual Consultations (optional)
Day 4
9:00-11:00 Continuation of longitudinal network analysis techniques
11:00-12:00 Network modeling and causal inference, network models in experimental designs: clinical trials example, structural causal models (SCMs)
12:00-1:00 Lunch Break
1:00-3:00 Continuation of network modeling and causal inference, network models in experimental designs: clinical trials example, structural causal models (SCMs)

 

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