2-months hands on training on SPSS

Paper 1: Introduction to SPSS Interface and Data Management.

Importing and exporting the file, data view, data validation, missing data management, coding and decoding the data, new variable creation, variable transformation, sorting and filtering the data, creating syntax file, data manipulation, data merging and appending, 

Paper 2: Descriptive Statistics, Data Exploration and Data Visualization

Central tendency, Measure of dispersion- Range, variance, standard deviation, interquartile range, measures of normal distribution-skewness and kurtosis.

 Data visualization-bar plot, histogram, boxplot, pie chart, line plot, Stacked Bar, Scatter plot, dealing with imbalanced data set, Data exploration- Data normalization, 

Paper 3: Introduction to Inferential Statistics, Chi-square test, t-test

Inferential statistics- hypothesis testing

t-test- Independent t- test, paired sample t – test, Welch's t-test, Mann Whitney u test, signed rank test, confidence interval. 

Paper 4: ANOVA, Regression Analysis, Outlier detection

ANOVA- one way and two way ANOVA, repeated measures ANOVA

Regression Analysis- correlation, simple linear regression, logistic regression, outlier detection.

Paper 5: Practice and Review

Project report writing. 

CALENDAR

Class stars: 1.3.24.

Class ends: 1.5.24

Class day: Monday, Wednesday and Friday.

TIME: 3 TO 4 PM 

MARKS DISTRIBUTION 

Assignment for theory: 4 theory papers X 15 marks=60

Project report: 30

Viva: 10

PEDAGOGY

#Data will be provided to students initially for hands on training in class. Later they will apply them on their own data set.

#One project proposal will be submitted initially by the students and they will submit their reports for evaluation.

#Students will share the assignment in Google classroom and share experience in the WhatsApp group.

SOFTWARE

https://www.ibm.com/spss

PROJECT FORMAT

Title page: Title, acknowledgement, Declaration, Contents.

Title: Analysis of value name like (Self Awakening) data using SPSS

Introduction: Definition of value, literature review of value. 

Method: 

Data source, Data structure (30 participants X 5 items= 150 observations), Data description.

Results and Interpretation. Tables and Figures.

1. Descriptive statistics include frequency distribution, percentage, mean, median, mode, and SD. 

2. Data visualization of descriptive statistics; histogram, box whisker plot,  barplot, pie chart. 

3. Inferential Statistics: Cross tabulation of 5 items. Item total correlation, data visualization of cross tabulation, and data visualization of simple regression where dependent variable is total score and independent variable will be the items.

Discussion 

Reference 

Appendix