Unleash the full potential of your data through predictive analysis, statistical learning, perceptual mapping, preference scaling, and dimension reduction techniques—including optimal scaling of your variables. SPSS Categories provides you with all the tools you need to obtain clear insight into complex categorical and numeric data, as well as high-dimensional data. For example, use SPSS Categories to understand which characteristics consumers relate most closely to your brand, or to determine customer perception of your products compared to other products you or your competitors offer.
You can visually interpret datasets and see how rows and columns relate in large tables of scores, counts, ratings, rankings, or similarities. This gives you the ability to:
The advanced procedures available in SPSS Categories enable you to perform additional statistical operations on categorical data.
SPSS Categories’ dimension reduction techniques enable you to clarify relationships in your data by using perceptual maps and biplots.
By using the preference scaling feature, you can further visualize relationships among objects. The breakthrough algorithm on which this procedure is based enables you to perform non-metric analyses for ordinal data and obtain meaningful results. The proximities scaling procedure allows you to analyze similarities between objects, and incorporate characteristics for objects in the same analysis.
Read more about the procedures included in SPSS Categories.
The data are a 2x5x6 table containing information on two genders, five age groups and six products. This plot shows the results of a two-dimensional multiple correspondence analysis of the table. Notice that products such as "A" and "B" are chosen at younger ages and by males, while products such as "G" and "C" are preferred at older ages.
SPSS Categories is available in English, Japanese, French, German, Italian, Spanish, and Simplified Chinese.
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