G A L I B M C D O P E F IBM SPSS Amosによる構造方程式モデリング 184 absolute fit index ................................................... 11 AGFI（Adjusted Goodness of Fit Index） ...... 13 AIC（Akaike Information Criterion） .... 17, 108 BIC（Bayesian Information Criterion） .......... 17 CFI（Comparative Fit of Index） ..................... 16 CMIN....................................................................... 12 comparative fit index ............................................ 11 Comparative Fit of Index ..................................... 16 configural invariance ..........................................146 Confirmatory Factor Analysis, CFA ..............5, 76 Covariance Structure Analysis .............................. 2 degree of freedom.................................................. 10 endogenous variables .............................................. 8 error variables........................................................... 7 exogenous variables ................................................ 8 Exploratory Factor Analysis, EFA ..................... 75 factor ........................................................................ 74 factor analysis......................................................... 74 2次因子分析モデル..................................111, 112 StatsGuild Inc. factor loading ......................................................... 77 factorial invariance.............................................. 146 fit index ................................................................... 11 GFI（Goodness of Fit Index） .......................... 13 independence model ............................................. 11 intercept .................................................................. 24 latent variable............................................................7 MIMICモデル............................................ 111, 112 modification index ..................................... 112, 132 multicollinearity .................................................... 59 multi-group analysis ........................................... 144 multiple index model ......................................6, 110 Multiple Indicator Multiple Cause model ....... 111 multiple population analysis of structural equation modeling ............................................................... 144 observed variable......................................................7 parsimony correction index ................................. 11 partial regression coefficient ............................... 25 Path Analysis ..................................................... 4, 26 path diagram..............................................................2 数字
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