The Statistical Package of Social Sciences (SPSS), allows the user to perform both descriptive and inferential statistics. The SPSS mainly produces its output in two forms: graphs and tables. For the descriptive statistics, the SPSS produces frequency tables, measures of central tendency and measures of dispersion tables. Frequency tables are always divided into 3 main columns: frequency (counts), percentage and cumulative frequency columns. The frequency tells the user the frequency of a given categorical or ordinal variable while the percentage column converts the frequency to a percentage. The most common table is the descriptive statistics table which mainly has as many columns as the user wishes since it is the user that decides which descriptive statistics are to be used to analyze the data such include the mean, standard error of the mean, standard deviation, minimum, maximum, range, kurtosis, skewness, variance and sum.
Hypothesis testing makes use of inferential statistics in which parametric or non-parametric tests are used. Parametric tests are statistical tests that make assumptions about the sample population while non-parametric tests do not make such assumptions. Examples of parametric tests include regression analysis, correlation test, t-tests and Anova test etc. Examples of non-parametric tests include Kolmogorov-Smirnov test, Kruskal Wallis test, and Friedman’s Anova test amongst others
The interpretation of outputs produced by the SPSS is usually complicated especially to the novice. This is as a result of statistical significance which involves comparing the P value of the given test to a significance level so as to either reject or “accept” the null hypothesis. Normally, if the P value is less or equal to the level of significance, we reject the null hypothesis and vice versa. However, there is more to SPSS outputs other than just the P value and that is where it gets complicated.
Although it seems easy, interpretation of the SPSS requires an spss expert so as not to offer misleading results.Poor interpretation of SPSS output will lead to make the wrong conclusions about a given dataset which is why you need the exerts at Statistics Guru to help you with such issues. Over the years we have helped students and researchers build confidence in our work by providing our expertise in SPSS. The most common SPSS outputs we often interpret include regression test outputs(OLS, simple, multiple, stepwise, backward and enter), Anova test output, moderation analysis outputs, t test (independent and dependent samples) outputs, cluster analysis outputs, correlation outputs and chi square test outputs amongst others. Other services offered by our experts at this juncture include;
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