SPSS and Applied Techniques
Duration
23rd -27th October to 9th-13th October
Time: 9:00 – 3:00pm
COURSE OBJECTIVE
At the end of the course, students should be able to:
- Distinguish between qualitative and quantitative data analysis and procedures
- Conceptualize, state, test hypotheses and explain the conclusions
- Distinguish between parametric and non-parametric analysis
- Select appropriate statistical techniques for data analysis
- Produce professional quality reports
- Data Analysis in the Research Process
- Types of Qualitative Data
- Transcription and analysis (summarizing, categorization and thematization; interpretation; constant comparative causal pathways, cross-site analysis, inductive analysis, concept mapping etc.)
- Presentation (text, matrices, charts, pictures, tables, sound)
- Quantitative Data management
- Scale of measurement – measure of central tendency
- Introduction to SPSS: File creation, Dialogue boxes, Definition of variables and management, Missing Values, Data entry, Saving and retrieving data, backing up files
- Data screening and cleaning, Reliability, Overview of Open-ended items & Coding Manual,
- SPSS: Coding and recording, Transformation – computation, merging files (cases or variables). Selecting cases – Conditions; Combining Logical Relationships, versions of files
- SPSS: Data processing and analysis
- Linkage to objectives, All instruments, Data summary,
- Exploration, Stem and leaf, Frequencies, Pivot table editing,
- Descriptives, Standardization, Percentiles and Quartiles
- SPSS: Inferential statistics
- Exploring relationships (correlation (Pearson and Spearman), regression, cross-tabs, chi-square – phi, Cramer’s V), Mann-Whitney U Test, Wilcoxon Signed Rank Test, Kruskal-Wallis Test
- Comparison between means – Paired, Independent samples, One sample – T, Z, etc tests, ANOVA (1,2 way), Effect size
- Excel:
- MegaStats tools
- Mathematical operations, Data transformation, Analysis tools, Insert functions, Descriptives, Skewness, Kurtosis, Percentiles & Quartiles, Standardization, Fractions, sumproduct
- Histogram, Other graphs (bar, pie, line, S&D, Lorenz)
- Inferential statistics – Sampling, T-test, Z-test, F-test, ANOVA (1,2 way), Difference between proportions
- Pivot table and chart report – One Variable, two variable tally, Grouping categories in charts
- Excel Logical Functions
