学术前沿讲座
报告题目 | Person-centered methods: Advanced contingency table analysis | ||
报告人（单位） | Prof.Mark Stemmler（Friedrich-Alexander-University Erlangen-Nuremberg） | ||
点评人（单位） | 吕鸿江（金洋娱乐软件安卓版） | 点评人（单位） | 鞠传静（金洋娱乐软件安卓版） |
时间地点 | 时间：2019年5月11日（周六 上午9：30） 地点：九龙湖经管楼A-501 | ||
报告内容摘要 | |||
This course takes an easy-to-understand look at a statistical approach called the person-centered method. Instead of analyzing means, variances, and covariances of scale scores as in the common variable-centered approach, the person-centered approach uses contingency tables to examine persons or objects grouped according to their characteristic patterns or configurations. In contingency tables, the observed patterns are ordered by their indices; a certain position in a table, denoted by a pattern or configuration, is called a cell. The main focus of the course will be on configural frequency analysis (CFA; Stemmler, 2014; von Eye, 2002), which is a statistical method that looks for over- and under-frequented cells or patterns. A pattern or configuration that contains more observed cases than expected is called a type; a pattern or configuration that is observed less frequently than expected is called an antitype. CFA resembles log-linear modeling: log-linear modeling seeks to find a fitting model including all important variables; instead of fitting a model, CFA examines the significant residuals of a log-linear model. In addition to the theoretical introduction, many data examples will be provided. The course will be using CFA-freeware (Alexander von Eye; voneye@msu.edu) and the R-package confreq. | |||
报告题目 | Using Configural Frequency Analysis as a Person-centered Analytic Approach with Categorical Data | ||
报告人（单位） | Prof.Mark Stemmler（Friedrich-Alexander-University Erlangen-Nuremberg） | ||
点评人（单位） | 葛沪飞（金洋娱乐软件安卓版） | 点评人（单位） | 周路路（金洋娱乐软件安卓版） |
时间地点 | 时间：2019年5月11日（周六 下午2：00） 地点：九龙湖经管楼A-501 | ||
报告内容摘要 | |||
Four models of configural frequency analysis are presented: (1) First-order configural frequency analysis, which is basically the analysis of a main effects log-linear model; (2) prediction configural frequency analysis, which defines one or more dependent variables; (3) two-group configural frequency analysis, which proposes that there is no association between discrimination variables and group membership; and (4) functional configural frequency analysis, which allows us to blank out certain outlier cells in order to test for the quasi-independence of the rest of the cross-table. The use of the open source Rpackage confreq for computational analysis is demonstrated. The advantages, as well as the limitations, of configural frequency analysis are discussed. Required text: Stemmler, M. (2014). Person-centered methods - configural frequency analysis (CFA) and other methods for the analysis of contingency tables. New York: Springer Briefs in Statistics. Stemmler, M. & Heine, J.-H. (2016). Using Configural Frequency Analysis as a Person-Centered Analytic Approach with Categorical Data. International Journal of Behavioural Development. DOI: 10.1177/0165025416647524. von Eye, A. (2002). Configural frequency analysis - methods, models, and applications. Mahwah, NJ: Lawrence Erlbaum. | |||
报告人简介 | |||
Mark Stemmler, Ph.D.Professor of Psychological Assessment，Friedrich-Alexander University of Erlangen-Nuremberg (FAU), Department Head of Psychology and Sport Science at the University of Erlangen-Nuremberg.AND Adjunct Professor, The Pennsylvania State University, College of Health and Human Development, Department of Human Development and Family Studies, USA. | |||
