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Both the KMO and Bartlett’s test of sphericity are commonly used to verify the feasibility of the data for Exploratory Factor Analysis (EFA). Kaiser-Meyer Olkin (KMO) model tests sampling adequacy by measuring the proportion of variance in the items that may be common variance. Values ranging between .80 and 1.00 indicate sampling adequacy (Cerny & ...


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It is likely that a Breusch-Pagan test shows such a level of statistical significance (i.e. p < .001) because you have a relatively large sample size (N = 15,000). Visual inspection does indeed show a triangular pattern, indicating potential heteroskedasticity. From my experience, I would be more inclined to use the visual interpretation as it is more ...


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I'm not sure why your advisor thought it was a good idea to remove a response option from the data after collection, as @RNM noted. As well, mean substitution is seldom a really good option for missing data. If you have to work with the altered data, one option you can do with SPSS if you have access to the MVA procedure is to use the EM imputation option ...


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To properly answer your question regarding automatic model differences requires a little bit of history https://autobox.com/pdfs/econometrics.pdf to explain some different approaches to ARIMA model identification. Model identification has and always will be an iterative process much like peeling an onion where clues are found and followed and possibly ...


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The fundamental identity residual $\equiv$ observed $-$ fitted implies that each distinct observed value defines a straight line with slope $-1$ in a plot of residual versus fitted and in particular that a sharp lower limit to observed values gives the lowest possible such line, i.e. a sharp diagonal bound to the configuration of data points in that plot....


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I'm not sure I have your exact ANOVA model clearly in mind. But it seems sufficiently complex that I doubt there is any specific nonparametric procedure that matches it. (For a one-way ANOVA, the Kruskal-Wallis test is a good match, for a simple block design there is the Friedman test. But there are not many specialized nonparametric tests for more complex ...


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For the Ljung-Box statistic, 18 lags are used in its computation, so you need to have at least 19 time points in order to get that test statistic. For the standard errors in the other output, you're getting standard errors for the Residual autocorrelation function for the model (your last output table). For the first two output tables, where the SEs are ...


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@ttnphns is right: the POSTHOC subcommand is only available for comparisons involving between-subjects factor levels, because many of the options there are only valid in that context. Also, if there are covariates in the model even with a between-subjects design, POSTHOC is not available. EMMEANS is the way to go in all of these cases.


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I don't believe there's a way to get +/- X SD lines. How exactly those would be computed isn't clear to me. If you mean lines at so many standard errors, those aren't directly available, though they could be fudged by using prediction intervals based on a bit of arithmetic to specify the coverage level corresponding to that many SEs. It is possible to plot ...


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First, you created missing data where there was none. Perhaps your supervisor had something particular in mind, but I can't see what it could be. The midpoint is not missing. Second, you used a very poor method of imputation - mean substitution. This makes the missing data much too regular. If you really insist on creating a problem then, at least, you ...


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The whole point of the statistics in the SPSS Exact Tests module, which includes the Exact and Monte Carlo results, is that they don't rely on asymptotic approximations, but either directly calculate (exact) or estimate (Monte Carlo) p values instead. So if the rules to which you're referring have to do with when you can use asymptotic approximations, they'...


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I match the R results in SPSS by inputting them as two columns. The R Kappa output refers to 11 subjects and 2 raters. As @ttnphns noted, the data input to R don't seem to correspond at all to what was input to SPSS. I'm pretty sure the R results would match those from SPSS if the data were input properly. I have to believe there's a way to input ...


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The models are indeed the same overall model, as shown by the identical R2, F, and sums of squares for the overall model. You could obtain the same results in UNIANOVA as in REGRESSION if you were to substitute all the variables you actually entered in REGRESSION as covariates in UNIANOVA. I expect that not only is the regression coefficient and test for ...


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