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# (p.217) Index

# (p.217) Index

Area under the curve (AUC), 127

Axioms of treatment, 7

Bias

consequential interpretations, 20

cross-cultural, 194

equivalence, 194

item, 195

method, 194

respondent's, 44

Chi-square goodness-of-fit test statistic, 158–159

*Clinical Measurement Package*, 7

Coefficient alpha, 63, 66, 184, 187, 192

equation for, 87

internal consistency based on, 85–96

stratified, 92–94

Concept, 25

Concept mapping, 25–27

(p.218)
Confirmatory factor analysis (

*continued*)model fit, 156

sample size, 64–65

violation of assumptions, 151

when to use, 181

Construct, 6, 35, 104, 183

dimensionality, 38

representation, 42

scale items, 40

social relevance, 27

Covariance, 85, 133

in confirmatory factor analysis, 147

implied, 138

matrix, 122, 139, 149, 150

in Comparative Fit Index, 159

predicted, 138

reproduced, 158

in Standardized Root Mean Square Residual, 160

unique elements in, 148

weighted, 138

in nested

*vs*. non-nested models, 168for PSCS subscales, 93

Cross-cultural assessment, 193–197

Cutting scores, 78

Dimensions, 38

Diversity, 193–197

Domain boundaries, 41–42

Domain scores, 121–123

Factor analytic models, 13, 131, 138, 140.

*See also*CFA, EFAequation for one factor model, 132

in relation to item response theory (IRT), 178

Factor axes, rotation of, 140–144

Factor extraction methods, 137

Factor loadings, 144, 147, 149, 153, 184

definition of, 140

in modification indices, 162

in multiple-group CFA, 172

in multiple indicators multiple causes (MIMIC), 171

in relation to item response theory (IRT), 178

Heterogeneous, 57

Index, 10

Indivualized rating scales, 38

Invariance

bilingual, 169

factorial, 175

differential item functioning (DIF), 170

in relation to item response theory (IRT), 180

measurement, 169

sample size, 62

Item response options, sensitivity, 50

Kuder-Richardson 20 (KR20), 86

Latent construct, 19

(p.220)
Latent factors, 141, 147, 157

in exploratory factor analysis, 144

in factor analytic models, 132–133

in item response theory (IRT), 179

in multiple indicators multiple causes (MIMIC), 170

in nested

*vs*. non-nested models, 168Latent factors (

*continued*)in parameter estimation and interpretation, 151–153

in principal components analysis, 137

Latent traits, 13, 64, 131, 132

in multiple indicators multiple causes (MIMIC), 170

in relation to item response theory (IRT), 180

Least square, 137–139

Likert-type category partitions, 47–50

Mplus, 151, 158, 160, 171

in modification indices, 162

in multiple-group CFA, 173

in parameter interpretation, 153

in relation to item response theory (IRT), 178

Multivariate normality, 151

Nomological net, 116

Overburdening, 40

Overdetermination, 64

Parental Self-Care Scale (PSCS)

CFA model for, 147

communality output for, 139

covariance matrices for, 93

description and original item pool of, 23–24

expert panel content validation for, 46

fit indices for, 169

reliability of, 90–94

scree plot for, 136

Population relevance, 57

Postcoding, 69–70

Practice effectiveness, 6

Precoding, 69–70

Principal axis factoring, 138–139

Principal components analysis, 137–138

Reasonable, 43

Reliability, 13, 63–64, 79, 165, 184

for FRS, 89–90

how to estimate, 82–96

internal consistency, 43, 71, 82, 184, 188

and power analysis, 65–66

in relation to item response options, 50

and the Spearman-Brown prophecy formula, 37

of multidimensional scales, 90–94

in relation to validity coefficients, 115

standards of, 94–95

theory of, 80–82

underestimation of, 92

of unidimensional scales, 89–90

variance, observed and true scores, 81–82

Response options, 43, 47–50, 87, 194

and readability, 36

in relation to precoding and postcoding, 69

Response rate, 75

Root Mean Square Error of Approximation (RMSEA), 158–160

in multiple-group CFA, 174

in nested

*vs*. non-nested models, 168Sample size, 62–65, 75, 100, 144–145, 186

in concept mapping, 26

effect on chi-square statistic, 158–159

effect on Mplus, 160

in item response theory (IRT), 177

Screening, 125–130

Scree plot, 135–137

Semantic differential, 47–51

Simple structure, 141, 145–146, 150

in modification indices, 162

in multiple indicators multiple causes (MIMIC), 170

in parameter interpretation, 153

in the PSCS, 164

Social relevance, consequential interpretations, 20

Standard error of measurement (

*SEM*), 13, 63, 95–96, 184, 188in multiple indicators multiple causes (MIMIC), 172

Standardized Root Mean Square Residual, (SRMR), 158, 160, 162

in multiple-group CFA, 174

in nested

*vs*. non-nested models, 168Three-indicator rule, 149

Translation, 193–197

t-rule, 148

Two-indicator rule, 149

Unstandardized scales, 38

Validity, evidence of, 10, 13, 99–100, 184, 190–191

bilingual, 197–199

known instruments, 124–125

predictive, 118

false-negatives, 126

false-positives, 126–127

incremental, 200

translation, 197–199

types of evidence, 101

Vignettes, 58–59