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Variable Cluster Analysis: A useful approach to identify underlying dimensions of a questionnaire Usree Kirtania, MS; Cynthia Davis, MS Institute for Community.

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Presentation on theme: "Variable Cluster Analysis: A useful approach to identify underlying dimensions of a questionnaire Usree Kirtania, MS; Cynthia Davis, MS Institute for Community."— Presentation transcript:

1 Variable Cluster Analysis: A useful approach to identify underlying dimensions of a questionnaire Usree Kirtania, MS; Cynthia Davis, MS Institute for Community Health Promotion, Nov 2006 Introduction _____________________________________________________ Variable Cluster Analysis, (implemented in SAS through PROC VARCLUS), is another variable reduction method that often has distinct advantages over the traditional Factor Analysis (FA) approach. This method borrowed some ideas from the Factor Analysis method and some from the Hierarchical Clustering method and produces either disjoint or hierarchical clusters. These distinct clusters from VARCLUS help to identify underlying dimensions of a questionnaire which are essential for developing a well constructed scale score. OBJECTIVE: To identify underlying dimensions of a questionnaire using Variable cluster analysis (VARCLUS) approach. VARCLUS procedure vs. Factor Analysis (FA) ______________________________________________ Fat related eating behaviors using Factor Analysis __________________________________________________ FA produced 7 factors. 62% total variation explained by these 7 factors. Higher fat food factor (8 items α=0.74).Chinese food factor (3 items α=0.65). Restaurant factor (4 items α=0.72). Milk fat factor (3 items α=0.70). Low/lean fat food factor (4 items α=0.44). Fruit as snack/dessert factor (2 items α=0.76). Adding fat factor (2 items α=0.44). Conclusions __________________________________________________ No estimating communalities makes the VARCLUS procedure simple. Due to distinct clusters, VARCLUS is an easier method to detect and to explain underlying dimensions compared to the Factor Analysis approach, which produces overlapping factors. So, we should consider the VARCLUS approach and use it more often along with FA because of it’s simplicity and interpretability. Contact: Usree Kirtania. MS. Statistical Data Analyst BROWN UNIVERSITY Fat related eating behaviors using VARCLUS procedure _________________________________________________ Preliminary VARCLUS suggested 7 distinct clusters. 59% total variation explained by these 7 clusters. Cluster items and 1 –R 2 ratio has been presented below. Data ________________________________________________ We applied the VARCLUS method in a 94 item food habit questionnaire (STFHQ) from the SISTERTALK study,which is a weight control intervention program for African American women (N=461).Each introductory item is followed by several behavioral items. We used 57 behavioral items in the analysis. Example: Introductory item:How often did you eat bacon or sausage? Behavioral item:How often was it low fat or turkey bacon? Multi level responses: (Almost always/often, sometimes, rarely, never). For all behavioral items higher score indicates higher fat intake behavior. Missing values generated in behavioral items due to response ‘never’ in an introductory item were imputed with zero. VARCLUS Procedure ______________________________________________________ X1, X2, X3, X4, X5 X1, X3, X4 X2, X5X1, X3X nd eigenvalue Threshold (Default) Divisive Clustering VARCLUSFactor Analysis (FA) MAXEIGEN=option using Correlation matrix OR PERCENT=option using Covariance matrix Estimate communalities using Squared Multiple Correlation (SMC) Number of clusters Clusters that meet 2 nd eigenvalue < specified threshold Number of factors Scree plot Kaiser-Guttman rule 1)All variables start in one cluster. 2) 2 nd eigenvalue > specified threshold => additional dimensions. 3) The initial cluster divided into two clusters. 4) The procedure stops when each cluster satisfies 2 nd eigenvalue < specified threshold criterion. 5) Variables have relatively high correlation with their own cluster and low correlation with other clusters. 6) VARCLUS uses the first principal component based on correlation matrix or the first centroid component based on covariance matrix. Rotation Ortho-oblique Rotation Varimax Promax Cluster representative (1-R 2 ) ratio (Lower is better) Factor representative Factor loading (Higher is better) 7) VARCLUS generates (1-R 2 own cluster ) 1-R 2 Ratio = (1-R 2 next closest cluster ) Higher fat food (  =0.74) Chick1 (0.39) Ffish1 (0.56) Sitdn6 (0.58) Ffood6 (0.60) Restaurant (  =0.72) Sitdn1 (0.41) Sitdn3 (0.41) Sitdn5 (0.52) Sitdn7 (0.50) Chinese food (  =0.65) Chins1 (0.32) Chins2 (0.46) Chins4 (0.50) Lean fat food (  =0.48) Grdmt1 (0.52) Grdmt2 (0.59) Redmt1 (0.62) Redmt3 (0.52) Milk fat (  =0.71) Milk1 (0.19) Milk21 (0.16) Fruit as snack/ dessert (  =0.76) Otdes4 (0.23) Snack3(0.23) Adding fat (  =0.59) Hotcr1 (0.49) Sandw1 (0.51) Potat3 (0.57)


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