Job Satisfaction Employee Turnover Can Research Proposal

Length: 4 pages Sources: 3 Subject: Careers Type: Research Proposal Paper: #93146649 Related Topics: Job Satisfaction, Book Of Job, Employee Morale, Employee Benefits
Excerpt from Research Proposal :



Table 2 Fit measures for models 1 and 2

Model

X[sup2]

X[sup2]/df

GFI

AGFI

IFI

SRMR

X[sup2] = Chi-Squared is rarely used by itself as a measure of model fit, since it is heavily influenced by the number of cases in the sample (Byrne, 1998).

X[sup2]/df = Chi-Squared divided by degrees of freedom and a small resulting value interpreted as a good fit, while a large value is generally seen as indicative of a bad or poor data-model fit. Usually, as a rule of thumb, a cut-off value of 3.00 to 4.00, and even as high as 5.00, is interpreted as indicative as a good data-model fit (Bollen, 1989, p. 278; Hayduk, 1987, p. 168; Mueller, 1996, p. 84).

GFI = Goodness of Fit is a measure of the amount of observed variance/covariance information that can be accounted for by the hypothesized model and values over 0.90 are seen as indicative of a good fit (Byrne, 1998; Jaccard & Wan, 1996).

AGFI = Adjusted Goodness of Fit Index is an adjusted measure of the Goodness of fit Index, which takes into account the degrees of freedom (Byrne, 1998; Joreskog & Sorbom, 1996) and values...

...

s over 0.90 are seen as indicative of an overall good model fit (Byrne, 1998).

NFI = Normed Fit Index is a normed incremental fit index which compares the tested model to that the independence and values greater than 0.90 are desirable (Byrne, 1994, 1998).

CFI = Comparative Fit Index is an incremental fit index that uses a noncentral chi-square distribution in assessing the fit of the model to the data (Byrne, 1998) and values over 0.90 are seen as indicative of a good model fit (Bentler, 1995).

IFI = Incremental Fit Index compares the tested model with a baseline model (Bollen, 1989) and value over 0.90 are seen as indicating an acceptable model to data fit (Jaccard & Wan, 1996).

SMSR = Standardized Root Mean Square Residual is the average differences between the observed and estimated correlations (Byrne, 1998) and values of 0.050 and lower indicates a good model fit (Sorborm & Joreskog, 1982).

Table 3 Standardized factor (measurement) coefficients for the latent constructs

Latent construct

Standardized factors

Latent construct and Standardized factor and indicators coefficient/loading indicators coefficient/loading

Task Variety (FTASK)

Autonomy/participation (FAUTO)

Task variety 1 (TASK1)

0.557(FN*)…

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