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CFA & EFA SU R VE Y AN D ME ASU R E ME N T D E VE L OP ME N T - PowerPoint PPT Presentation

Dec 20, 2022 •512 likes •802 views

CFA & EFA SU R VE Y AN D ME ASU R E ME N T D E VE L OP ME N T IN R George Mo u nt Data anal y st to the stars CFA and model de v elopment 1 Hinkin , T . R . (1998). A brief t u torial on the de v elopment of meas u res for u se in s u r v e

  1. CFA & EFA SU R VE Y AN D ME ASU R E ME N T D E VE L OP ME N T IN R George Mo u nt Data anal y st to the stars

  2. CFA and model de v elopment 1 Hinkin , T . R . (1998). A brief t u torial on the de v elopment of meas u res for u se in s u r v e y q u estionnaires . Organi z ational research methods , 1(1). SURVEY AND MEASUREMENT DEVELOPMENT IN R

  3. CFA v ers u s EFA EFA CFA Ho w man y factors are necessar y to e x plain Do n u mber of factors match w hat is relations among items ? e x pected on the basis of theor y? Theor y de v elopment H y pothesis testing SURVEY AND MEASUREMENT DEVELOPMENT IN R

  4. Introd u cing la v aan la ( tent ) v a ( riable ) an ( al y sis ) Co u rse : Str u ct u ral Eq u ation Modeling w ith la v aan in R Use =~ to assign items to factors Fit the model to the data SURVEY AND MEASUREMENT DEVELOPMENT IN R

  5. Initial CFA # Import lavaan library(lavaan) # Define the model brand_qual_9_CFAmodel <- 'VAL =~ reason_price + happy_pay + good_deal PERF =~ serious + leader + strong_perform FUN =~ trendy + latest + tired_r' SURVEY AND MEASUREMENT DEVELOPMENT IN R

  6. Initial CFA # Fit the model to the data brand_qual_9_CFA <- cfa(model = brand_qual_9_CFA_model, data = brand_qual_9) # Summarize results -- # Include fit measures and standardized estimates summary(brand_qual_9_CFA, fit.measures = TRUE, standardized = TRUE) SURVEY AND MEASUREMENT DEVELOPMENT IN R

  7. Inspecting o u r res u lts : loadings inspect(brand_qual_9_CFA, "std")$lambda VAL PERF FUN reason_price 0.952 0.000 0.000 happy_pay 0.934 0.000 0.000 good_deal 0.792 0.000 0.000 serious 0.000 0.920 0.000 leader 0.000 0.850 0.000 strong_perform 0.000 0.797 0.000 trendy 0.000 0.000 0.839 latest 0.000 0.000 0.898 tired_r 0.000 0.000 0.649 SURVEY AND MEASUREMENT DEVELOPMENT IN R

  8. Plotting o u r res u lts # load semPlot library(semPlot) # diagram model semPaths(brand_qual_9_CFA) SURVEY AND MEASUREMENT DEVELOPMENT IN R

  9. CFA in la v aan : Let ' s practice ! SU R VE Y AN D ME ASU R E ME N T D E VE L OP ME N T IN R

  10. CFA ass u mptions & interpretation SU R VE Y AN D ME ASU R E ME N T D E VE L OP ME N T IN R George Mo u nt Data anal y tics ed u cator

  11. Inspecting for m u lti v ariate normalit y library(psych) # Mardia test of multivariate normality mardia(brand_qual_9) Mardia tests of multivariate skew and kurtosis Use describe(x) the to get univariate tests n.obs = 508 num.vars = 9 b1p = 60.32 skew = 5107.33 with probability = 0 small sample skew = 5143.55 with probability = 0 b2p = 212.51 kurtosis = 90.91 with probability = 0 SURVEY AND MEASUREMENT DEVELOPMENT IN R

  12. Adj u sting for non - normalit y # Fit model to the data using robust standard errors bq_cfa <- cfa(model = bq_model, data = brand_qual_9, estimator = "MLR") summary(bqcfa, standardized = T, fit.measures = T) SURVEY AND MEASUREMENT DEVELOPMENT IN R

  13. Adj u sting for non - normalit y Meas u re Val u e to look for Comparati v e � t inde x ( CFI ) >.9 T u cker - Le w is inde x ( TLI ) >.9 Root mean sq u are error of appro x imation ( RMSEA ) < .05 SURVEY AND MEASUREMENT DEVELOPMENT IN R

  14. CFA fit meas u res Meas u re Val u e to look for chi - sq u are p < .05 ( b u t be w are sample si z e !) CFI > .9 T u cker - Le w is Inde x ( TLI > .9 RMSEA < .05 SURVEY AND MEASUREMENT DEVELOPMENT IN R

  15. Inspecting o u r fit meas u res # Total number of fit measures in lavaan output length(fitMeasures(brand_qual_9_CFA) [1] 42 SURVEY AND MEASUREMENT DEVELOPMENT IN R

  16. Inspecting o u r fit meas u res # Access individual fit measures fitMeasures(brand_qual_9_CFA, fit.measures = c("cfi","tli")) cfi tli 0.970 0.955 SURVEY AND MEASUREMENT DEVELOPMENT IN R

  17. Inspecting o u r estimates # Access estimates in a data frame library(dplyr) standardizedSolution(brand_qual_9_CFA) %>% names() "lhs" "op" "rhs" "est.std" "se" "z" "pvalue" "ci.lower" "ci.upper" SURVEY AND MEASUREMENT DEVELOPMENT IN R

  18. Inspecting o u r estimates # Get names, estimates, p values for F2 manifest items standardizedSolution(brand_qual_9_CFA) %>% filter(op == "=~" & lhs == "F2") %>% select(rhs, est.std, pvalue) rhs est.std pvalue 1 happypay 0.9320353 0 2 reasonprice 0.9522508 0 3 gooddeal 0.7921012 0 SURVEY AND MEASUREMENT DEVELOPMENT IN R

  19. What if the CFA doesn ' t fit ? Ret u rn to EFA to learn more abo u t y o u r scale ( see " Factor Anal y sis in R ") E x amine CFA : modi � cation indices , factor v ariances , etc . ( see " SEM w ith lavaan in R ") SURVEY AND MEASUREMENT DEVELOPMENT IN R

  20. Let ' s practice ! SU R VE Y AN D ME ASU R E ME N T D E VE L OP ME N T IN R

  21. Constr u ct Validit y SU R VE Y AN D ME ASU R E ME N T D E VE L OP ME N T IN R George Mo u nt Data anal y tics ed u cator

  22. The " three C ' s of v alidit y" SURVEY AND MEASUREMENT DEVELOPMENT IN R

  23. Constr u ct v alidit y: Targeting the latent v ariable 1 h � ps ://u pload .w ikimedia . org /w ikipedia / commons /5/5 d / Reliabilit y_ and _v alidit y. s v g SURVEY AND MEASUREMENT DEVELOPMENT IN R

  24. Con v ergent & discriminant v alidit y # c_sat_sem is a lavaan CFA model library(semTools) reliability(c_sat_sem) F1 F2 F3 total alpha 0.7787792 0.6893959 0.7757958 0.8144735 omega 0.7788456 0.6896567 0.7757469 0.8624756 omega2 0.7788456 0.6896567 0.7757469 0.8624756 omega3 0.7761082 0.6873162 0.7744738 0.8726313 avevar 0.5085462 0.5233741 0.5371941 0.5235427 SURVEY AND MEASUREMENT DEVELOPMENT IN R

  25. Meas u ring constr u ct v alidit y # c_sat_sem is a lavaan CFA model library(semTools) reliability(c_sat_sem) F1 F2 F3 total alpha 0.7787792 0.6893959 0.7757958 0.8144735 omega 0.7788456 0.6896567 0.7757469 0.8624756 omega2 0.7788456 0.6896567 0.7757469 0.8624756 omega3 0.7761082 0.6873162 0.7744738 0.8726313 avevar 0.5085462 0.5233741 0.5371941 0.5235427 SURVEY AND MEASUREMENT DEVELOPMENT IN R

  26. Constr u ct v alidit y: interpretation F1 F2 F3 total alpha 0.7787792 0.6893959 0.7757958 0.8144735 omega 0.7788456 0.6896567 0.7757469 0.8624756 omega2 0.7788456 0.6896567 0.7757469 0.8624756 omega3 0.7761082 0.6873162 0.7744738 0.8726313 avevar 0.5085462 0.5233741 0.5371941 0.5235427 Con v ergent v alidit y: AVE ( avevar ) > .5 Discriminant v alidit y: CR ( omega ) > .7 alpha is Cronbach ' s alpha (>.7) SURVEY AND MEASUREMENT DEVELOPMENT IN R

  27. Let ' s practice ! SU R VE Y AN D ME ASU R E ME N T D E VE L OP ME N T IN R

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