SPSS for your research workshop

This workshop will focus on the various ways SPSS can be used in research. It will discuss the common statistical analyses and the appropriate use of these analyses to answer research questions and test hypotheses. Using hands-on exercises, participants will learn how SPSS can be used to screen and explore data, test for normality and equality of variance and execute basic commands. Following this, inferential data analyses including parametric tests such as Pearson Product Moment correlation, t-tests and One-way and Three-way ANOVA, ANCOVA as well as linear regression with moderation and mediation analysis. Non-parametric tests such as Chi-square, Mann-Whitney and Wilcoxon, Friedman’s ANOVA and Kruskall-Wallis will be also be covered. Discussions and hands-on exercises will include using SPSS to ascertain the reliabilities (Item Analysis, Split-Half, Kuder-Richardson, and Cronbach Alpha) and the various indices of validity of instruments. The workshop will also help participants to interpret correctly the SPSS output of these inferential data analyses, tabulate and report the results in the format acceptable for publication in your research, thesis or dissertation.
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We will introduce the such example of the steps that will be used in your scientific study can be summarised as follows:
Define your variables
Enter data
Run frequency and descriptive analysis
Data editing
Reliability analysis
Transformation of data
Exploratory data analysis (EDA) dan normality assessment
Data analysis for hypothesis testing
Modeling technique and simulation of data
The content of our Workshop:
Application of online data collection process using google drive and website method
Descriptive analysis = frequency, percentages, mean ± s.d, Level, median, mode, IQR, range, and others
Reliability test = Cronbach Alpha, inter-rater correlation
Normality test = Shapiro Wilk, Kolmogorov Smirnov, Histogram with normality curve, Q and Q plot, Boxplot, Skewness, and Kurtosis
Parametric test = t-test, ANOVA, Pearson Correlation, Regression, Simple Linear Regression
Non-parametric test = Mann Whitney U, Kruskal-Wallis, Kendal Tau and Spearman Correlation, Logistic Regression, Multiple Logistic Regression, Odd Ratio 95% CI.
Pre and Post-study = Pair t-test, Mc Necmar, Wilcoxon test
Crosstab = Chi-Square
Who can benefit from this course?
This workshop is offer for postgraduate students who are involved in quantitative data analysis. If you are involved in qualitative research, you might expand your idea to include quantitative techniques.

This workshop will also be valuable for researchers, academicians, and consultants involved in quantitative data analysis.

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