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The higher the reliability of the variables, the higher the correlations and the communality The higher the validity of the variables, the more meaningful the results For signi cance testing, uniqueness scores should be normally distributed Variables should have similar distributions in the sample for maximum correlations They need not be normally distributed, but a variable with a skew incompatible with the majority of the other variables should be avoided All variables need to have some cases that score high and some that score low Normal distribution is ne, but it is not desired if it obscures true highs and true lows This avoids restriction of range, which lowers observed correlations and so weakens the factor structure The sample size needs to be large enough for stable correlations.

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Before the plasmode and simulation studies, the best guess was that the N needed would be a function of the number of variables being analyzed Unlike multiple regression analysis and many previous discussions (eg, Nunnally, 1967; Gorsuch, 1974, 1983), factor analytic accuracy appears to be relatively independent of the number of variables (with the exception that, for mathematical reasons, the total N must always be larger than the total number of variables) However, both plasmode and simulation studies suggest that the N and the purpose of the study are crucial The N gives the stability of a correlation, and stability increases as the square root of the N decreases A zero correlation with an N of 100 has a standard error of 10, 150 is 08, 200 is 07, 300 is 06, and 400 is 05 This is a reasonable guide to sample size.

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Here we introduce a new concept: disassociated channel signaling. Up to now we have only considered associated channel signaling. In other words, the signaling is carried right on its associated voice channel, whether in-band or out-of-band. Figure 7.5 illustrates two concepts: associated channel and separate channel signaling, but still associated. E-1 channel 16 is an example. It is indeed a separate channel, but associated with the 30-channel group of traf c channels. We will call this quasi-associated channel signaling. Disassociated channel signaling is when signaling travels on a separate and distinct route than the traf c channels for which it serves. CCITT Signaling System No. 7 uses either this type of signaling or quasi-associated channel signaling. Figure 7.6 illustrates quasi-associated channel signaling, whereas Figure 7.7 shows fully disassociated channel signaling.

Because the purpose of a study is generally to distinguish between observed correlations of 30 and 40, for example, the safe sample size is 400 If one just wishes to determine which correlations are different from zero and is only interested in correlations 30 and higher, an N of 150 is reasonable A larger sample is needed for item factor analysis because one needs to differentiate between correlations differing by only 10 (N 400) and to reduce capitalization on chance in item selection The number of variables that each factor is expected to load should be in the range of three to six Fewer than three variables makes a factor dif cult to de ne, so using four to six is better.

Planning teams have to determine which concepts produced in a brainstorming session warrant further consideration. This task is not always easy, but if the team refers to stated goals and objectives, then the job is much simpler. In this case, the overall purpose of the program would be to maximize sales by the front of ce staff of front of ce, food and beverage department, gift shop, and health facilities products and services. The team must decide which area or areas would be most pro table.

Simulation studies have suggested more variables be used, but these are only when there are available new variables that are truly different from the original ones, except for being loaded by the same factors Experience suggests that such a situation seldom occurs, and the variables added after the rst six lead to minor factors More than six variables can lead to problems due to the sensitivity of factor analysis In EFA, a factor with more than.

1.13 Based on the scree plot shown in Figure E1.13, how many components should be extracted using the scree plot criterion Now, based on the three criteria, work toward a decision on the number of components to extract. 1.14 (a) Based on the following rotated component matrix; provide a quick pro le of the rst four components. (b) If we extracted an sixth component, describe how the rst component would change. (c) What is your considered opinion as to the usefulness of applying PCA on this data set

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