The tool should be able to identify seasonality or if there is none to be found. You shouldn't need to prescreen your data. I think this is just trying to avoid bad answers and under the guise of it being "faster". They then tell you that it will run faster if you know there is no seasonality. The online tutorial section called "Examining the data" talks about how Modeler can find the best seasonal models or nonseasonal models.
This makes us wonder why there is no challenging data to stress the system here?įor series 4 and 5 both are find to have seasonality. The 5 examples have no variability and would be categorized as "easy" to model and forecast with no visible outliers.
We went through the first 5 "broadband" examples that come with the trial that are set to run by default. There are 2 sets of time series examples included with the 30 day trial. We would be glad to hear any opinions(as always) differing or adding to ours.
We tested it and have more questions than answers. Then you could add year as a categorical predictor or factor in your GLM analyses.IBM released version SPSS Modeler 18 recently and with it a 30 day trial version. To incorporate year into analyses, you'd probably want to go back to the original data with all three years in a single dataset, with cases for a given year properly identified, then re-impute data, with year included as an imputation variable. There's lots of information about those on the Internet.
The pooling of results is done using what are known as Rubin's rules.
Pooling algorithms are given in the Multiple Imputation Pooling Algorithms chapter of the IBM SPSS Statistics Algorithms manual, which is available online (in the program, click Help>Documentation in PDF Format, select English or other desired language, then scroll down to the Manuals section and look for that title). You might do this by doing some averaging or something, but you'd be missing some of the value of multiple imputation (as you'd be eliminating between-imputation variability, which is integral to the methodology).Īs noted above, there's no pooling of datasets, only pooling of analysis results from different completed datasets. Pooling is done on the results of the analyses for the separate completed datasets. There is no pooled dataset with multiple imputation in SPSS or any other software. if they just take the averages of the values assigned to each case, I could write a script that does that, but I'm unsure if this is what's going on in the background)