STATS CAMP
Introduction
Series of courses for accelerated statistical continuing education
INTEREST CATEGORY: MARKETING RESEARCH
POSTING TYPE: Events
Author: Zachary Stickley
Accelerated Statistical Continuing Education
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.
JUNE 6 10 & 13 17, 2022
Internationally recognized award-winning instruction focused on advanced statistical training at the graduate and post-graduate level. Stats Camp statistics training seminars are on point with current trends and best practices in modern advanced statistical analysis. Get accessible and hands on instruction that delivers practical value for students, researchers, and institutions.
JUNE 6 10
Instructors: Mauricio Garnier-Villarreal, Ph.D. & Esteban Montenegro-Montenegro, Ph.D.
Tired of testing the same old null hypothesis over and over again? Want to test your actual research hypothesis? The Bayes method strongly believes the null of zero is a weak hypothesis. Put prior knowledge and research findings to good use by informing your Bayesian model. This seminar will introduce you to the prevailing best practices for Bayesian estimation including structural equation modeling (SEM), class examples, and direct application to your research questions using our data or yours! We will discuss what you need to run your first Bayesian model and create Bayesian results that could directly be used in your dissertation/papers.
Instructors: Leslie Echols, Ph.D. & Michael D. Siciliano, Ph.D.
Survey a variety of approaches to collecting and analyzing network data at single and multiple points in time using R software. Topics include basic statistics and visualization, network regression/QAP, exponential random graph models (ERGMs), and stochastic actor-oriented models (Rsiena). The workshop will consist of a mixture of classroom teaching and hands-on computer work. As such, this is a great introduction to R for anyone! Network data will be provided for the lab activities and participants will conduct some type of analysis every day. This is an applied seminar that will take you from novice to proficient in 5 days!
Instructor: Whitney Moore, Ph.D.
This seminar will introduce participants to the prevailing best practices for direct applications of basic finite mixture modeling to cross-sectional data, specifically latent profile analysis (LPA) also known as latent class cluster analysis (LCCA), in terms of model assumptions, specification, estimation, evaluation, selection, and interpretation. Models that allow for the inclusion of correlates and predictors of latent class membership as well as distal outcomes of latent class membership will be presented. The seminar will also explore hybrid latent variable models that include both latent factors and latent classes (termed factor mixture models) and will touch briefly on some longitudinal extensions of mixture modeling, as time allows. The implementation of these models in the most recent version of the Mplus software will be demonstrated throughout the seminar.
Instructor: Noel Card, Ph.D.
Systematic review and meta-analysis are techniques used to synthesize and summarize large bodies of research literature. Compared to results from a single primary study, results from a meta-analysis provide greater generalizability, increased precision, and the ability to explore heterogeneity across studies. You will use the latest techniques and technical tools to conduct a high-quality systematic review and meta-analysis. You will learn hands-on, practical, and applied approaches to conducting reviews by combining lectures with practice material designed to enable you to conduct future meta-analyses.
Instructor: Alex Schoemann, Ph.D.
Everything is nested, so you need something more than multiple regression or analysis of variance to get the job done! Nested data structures can include students within classrooms, professionals within corporations, patients within hospitals, or repeated observations from the same person. Multilevel modeling (MLM) is built to handle this kind of data. You will use real datasets and the R software environment to learn how to analyze multilevel data sets and interpret results of multilevel models.
Instructor: Mwarumba Mwavita, Ph.D.
Trying to get that grant or contract?! You need to have a rigorous, evidence-based plan to secure that funding. Almost all non-scientific evaluations will be unawarded or quickly dismissed. You need to assess your research project or program by conducting a rigorous evaluation of it. Fields such as sociology, psychology, education, human development, marketing, business, biology, medicine, political science, communication, governmental and nonprofit agencies, will benefit from the seminar.
Instructor: Daniel Bontempo, Ph.D.
R is a freely available, open-source software platform that is growing in both popularity and capacity across many fields of science. Some have found the learning curve to be steep, but were here to help you get over that curve! Once you move into the R world, youll find that it can be used for all stages of data analysis, from wrangling and cleaning, to a wide variety of statistical analyses, to generating quality visualizations ready for publication!
Instructors: Todd D. Little, Ph.D. & Elizabeth Grandfield, Ph.D.
Do you want to take your measurement to the latent level? Well, this is it, you have found it, the foundation to what you need to know for latent variable modeling – structural equation modeling (SEM)! Most campers report their prior training was insufficient and/or outdated. We will introduce you to the current techniques and advances in SEM as well as guide you through the steps to craft an exquisite SEM model.
JUNE 13 17
Instructor: Gitta Lubke, Ph.D.
An intermediate 5-day course introducing several popular data mining approaches such as regression-based methods (ridge and lasso regularized regression, regression splines), tree methods (random forests, boosted trees), and support vector machines, and their application to empirical data. The course combines lectures and hands-on practice using R.
Instructors: Todd D. Little, Ph.D. & Whitney Moore, Ph.D.
Do you have repeated measurements? Have you collected data over multiple timepoints? Do you need help designing your longitudinal study? If so, this is your course! Let us help you appropriately design your longitudinal study and analyze your data in the SEM latent variable framework using Longitudinal Structural Equation Modeling (LSEM). This framework will allow you more flexibility in evaluating your research questions over time as well as test assumptions that traditional techniques like ANOVA ignore.
Instructor: Alex Schoemann, Ph.D.
An introductory 5-day course on using R software for common analytic methods in behavioral and social sciences. Topics covered include, regression, mediation and moderation, multilevel modeling (MLM), factor analysis and structural equation modeling (SEM).
Instructor: Elizabeth Grandfield, Ph.D.
The course starts with a brief review of structural equation modeling with emphasis on the specific way Mplus is used to specify and estimate models, then discusses some ways to deal with warnings and error messages. We work with model specification and comparisons, multigroup models, DIF testing, moderation, and how to deal with Mplus defaults. We will test predictive hypotheses and mean differences, then cover more advanced topics related to longitudinal data along with its additional assumptions and how to fit longitudinal SEM models in Mplus. The last day provides time for an additional topic based on camper requests (i.e. MLM, Power Analysis, etc.).
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Mailing address: 3014 23rd Street, Lubbock TX 79410