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New PDF release: Advances in Configural Frequency Analysis (Methodology In

By Alexander von Eye PhD, Patrick Mair, Eun-Young Mun PhD

Utilizing real-world info examples, this authoritative booklet exhibits the way to use the most recent configural frequency research (CFA) options to research specific facts. many of the suggestions are offered the following for the 1st time. unlike tools that target relationships between variables, akin to log-linear modeling, CFA permits researchers to judge ameliorations and alter at the extent of person cells in a desk. Illustrated are how you can determine and attempt for telephone configurations which are both constant with or opposite to hypothesized styles (the kinds and antitypes of CFA); keep an eye on for power covariates that would impact saw effects; boost cutting edge prediction versions; deal with questions of moderation and mediation; and research extensive longitudinal info. The publication additionally describes loose software program purposes for executing CFA. (20110302)

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Extra resources for Advances in Configural Frequency Analysis (Methodology In The Social Sciences)

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Examples apply CFA of rater agreement to data on the assessment of qualification and fit of job applicants. 1 Rater Agreement CFA So far, exploratory applications of CFA scouted cross-classifications with the goal of finding types and antitypes, with no constraints concerning the location in the table on which to focus. CFA of rater agreement can proceed in a different way. In agreement tables, particular cells indicate agreement, and other cells indicate disagreement. CFA of rater agreement can focus on either or both (von Eye & Mun, 2005, 2006).

For two raters, this model is ˆ = λ + λA + λB + log m λk , k where the first three terms on the right-hand side of the equation are the same as in the first order CFA base model. The summation term describes the vectors needed to blank out the agreement cells. In the typical case, J such vectors are needed in a model, with J being the number of rating categories, and k = 1, . . , J. Types that result from this model indicate disagreement beyond chance, and antitypes indicate lack of disagreement beyond chance (for adaptations of coefficients of agreement to questions concerning disagreement, see von Eye & von Eye, 2005).

Instead, we ask whether types and antitypes exist that contradict the equal weight agreement model. If such types and antitypes can be identified, they indicate local associations. Just as with zero order and first order CFA of rater agreement, these associations suggest systematic patterns in the joint frequency distribution of the raters. When Tanner and Young’s model is used, however, types and antitypes indicate that the hypothesis of equal weights in the agreement cells allows one to explain only part of the variation in the cells of the agreement table.

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