X hits on this document

click on “Load Preferences”; (4) click on “Factory Settings.” If the ... - page 8 / 44

398 views

0 shares

8 / 44

how much of the variance in the variables you have factor analyzed are explained by all of the factors at the point (i.e., the first entry tells how much of the variance is explained by factor 1 while the second entry tells how much of the variance is explained by factors 1 & 2 together).  The score in the “uniqueness” column tells how much of the explained variance is unique to that variable.  For example, a score of .61 would indicate that 61% of the variance explained by that particular variable is not explained by the other variables.  If you then type “rotate” (default approach – varimax with orthogonal factors – i.e., the factors are not correlated with each other) it will maximize the fit of the dominant variables on that factor.  This setting is recommended when you want to identify variables to create indexes or new variables without inter-correlated components. To create new variables (after running “factor” and “rotate”) type: predict factor1 factor2 (you can use whatever names you want to the right of “predict”).  They will now appear as variables.

polychoric  a3a- a3o (need to add this command by typing findit polychoric) Note: tetrachoric for dichotomous variables

Mokken Scaling:Mokken scaling is an iterative scale-building technique, and

as it is non-parametric is especially suitable for skewed and binary items. It is based on Guttman scales, which are unidimensional, ordinal scales of binary items along a continuum. A positive answer to one item of a certain ‘difficulty’ indicates that all other items of lesser difficulty have also been

answered positively. For example, a positive response to one particular (rare) item indicates that other (more common) items have also been endorsed. Mokken scaling can also use polytomous items, and is a probabilistic version of Guttman scaling. Loevinger’s H-coefficient is used for interpretation. By convention, 0.3 ≥ H < 0.4, 0.4 ≥ H < 0.5 and H ≥ 0.5 indicate weak, moderate and strong scales respectively. Higher

H values indicate higher item discrimination power, and thus more confidence in ordering of respondents. The H-value equals [1 – (observed Guttman errors/predicted Guttman errors)]. Expected Guttman errors are the probability that the items are chosen by chance, while observed Guttman errors are the number of times items are endorsed as if not in an

ordered sequence. Therefore, a coefficient of ≤ .4 demonstrates a scale with items with a 60% rate of Guttman errors. Following a recommended procedure, which involves increasing the coefficient value until the most interpretable solution is found, items that demonstrate poor discriminability are excluded from the scale. Results can be compared to factor

analysis. In general, factor loadings larger than .5 result in H-coefficients greater than .3. Reported scales are ordered in terms of difficulty, ie. the most infrequently endorsed items feature at the top.(Frank Doyle, et. al., “Exhaustion, Depression and Hopelessness in Cardiac Patients: A Unidimensional Hierarchy of Symptoms Revealed by Mokken Scaling,” Royal College of Surgeons in Ireland, 2011, pp. 29-30).  “Loevinger coefficients Mokken (1971) proposed to measure the quality of the pair of

 Document views 398 Page views 461 Page last viewed Sat Dec 03 12:28:44 UTC 2016 Pages 44 Paragraphs 843 Words 17107