
Differentiate descriptive statistics from inferential statistics by describing population characteristics and providing a summary; include mean, median, and mode, and visualize with pie charts and bar graphs.
Explore descriptive and inferential statistics, focusing on drawing population conclusions from samples, guided by hypotheses and practical sampling, with data collection cost considerations.
Explore descriptive statistics with practical examples—mean, median, mode, and standard deviation—using IBM SPSS, and learn to present demographic profiles, frequency and cross tabulations before inferential analysis.
Learn how inferential statistics use SPSS to frame hypotheses about populations from samples, distinguishing null and alternate hypotheses and descriptive versus relational claims.
Learn to test null hypotheses in data analysis using SPSS, distinguish between reject and fail to reject, and interpret results with H0 and the alternative.
Use SPSS to test a null hypothesis with the p-value method, compare with a 0.05 significance level, and decide to reject or fail to reject in favor of the alternative.
Collect data using a Google form, clean and enter responses into SPSS, and interpret outputs while grounding analyses in hypothesis concepts and significance levels.
Learn to clean data for SPSS by removing irrelevant columns, converting text to numeric codes, handling missing data and outliers, and coding variables like gender and income.
Import data from Excel into SPSS, switch between data view and variable view, and code gender and marital status on scale, ordinal, or nominal levels using a Likert scale.
Master recoding a negative life satisfaction item into a positive scale in SPSS using transform, then assess reliability and relationships with Pearson or Spearman correlations after normality checks.
Aim
This course is aimed at better understanding the application of statistical concepts in research during data analysis stage which can enable the research scholars, students and faculty members to undertake thesis, dissertation and research paper.
Expected Outcome:
1. The workshop shall offer you the basic foundation for statistical analysis using SPSS.
2. The participants will be clear with applying appropriate statistical test for the given
data.
3. The participants shall be familiar data entry, data preparation, data analysis techniques
and hypothesis testing using SPSS.
4. Detailed Interpretation of the Output results.
Pedagogy:
The program shall be focused mainly on application of statistics for analyzing the data. The
session will be a blend of theory and practical with more importance to hands on exercise. Input
data shall be given to the participants to analyse and come out with meaningful results. SPSS
shall be used as a tool for data analysis. Equal importance shall be given to the concepts behind
the test and interpretation of the result.
Target Audience:
1. Research Scholars
2. Management Students
3. Faculties
SPSS as a tool for Data Analysis:
SPSS is one of the most widely used statistical software which is being recognised by the research community worldwide. Thanks to its user friendly Graphical User interface which has made data analyses much easier to deal with few clicks to get the required output. No matter how user friendly it is, one cannot neglect the importance of underlying fundamental statistics behind it to understand which type of test to use to what type of data and how to interpret the same. As a researcher, one has to be familiar with both the tools and techniques of data analysis and theory behind it. The two day national work shop on data analysis technique bridge the gap between all three dimensions of data analysis-Tools, Techniques and Theory behind it.