By Alexander von Eye PhD, Patrick Mair, Eun-Young Mun PhD
Utilizing real-world facts examples, this authoritative ebook indicates the best way to use the most recent configural frequency research (CFA) innovations to research express info. many of the ideas are provided right here for the 1st time. not like tools that concentrate on relationships between variables, comparable to log-linear modeling, CFA permits researchers to judge ameliorations and alter at the extent of person cells in a desk. Illustrated are how one can establish and try for mobilephone configurations which are both constant with or opposite to hypothesized styles (the varieties and antitypes of CFA); keep an eye on for power covariates that may impression saw effects; enhance leading edge prediction versions; tackle questions of moderation and mediation; and learn in depth longitudinal info. The booklet additionally describes loose software program purposes for executing CFA. (20110302)
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Additional info for Advances in Configural Frequency Analysis (Methodology In The Social Sciences)
It is σ2r = Npr [(1 − pr ) − (N − 1)(pr − p˜r )], where p is the same as for the binomial test. Lehmacher’s test requires that p be estimated, based on a main effect model. To illustrate the estimation ˜ consider a table that is spanned by three variables. For this case, the of p, estimate is (mi.. − 1)(m. j. k − 1) , p˜ ijk = (m − 1)d where i, j, and k index the categories of √ the three variables (d = 3) that span the table. Using the exact variance, mˆ r can be replaced by the standard normal ˆr mr − m zL,r = .
The second set involves specifying series of base models that, taken together, allow one to answer more complex questions. 2 Configural Analysis of Rater Agreement To illustrate the focus that CFA places on individual cells instead of aggregate-level appraisals of characteristics of cross-classifications, Chapter 2 introduces CFA of rater agreement. In contrast to such measures as κ, which present general statements about agreement beyond chance, CFA allows researchers to identify four groups of cells.
Each rater used six rating categories. Therefore, five effects coding vectors are needed per rater to capture the main effects of the two raters. The main effect model for rater independence, which is also used to calculate Cohen’s κ, uses the constant vector and the main effects vectors. The third block consists of just one column. This is the vector that is added to the first two blocks for Tanner and Young’s equal weight agreement model. Using this vector, it is proposed that the diagonal cells are different from the rest of the table, and that the weights of the diagonals cells are the same.