Hi Nishita,

Your question is quite unclear. Could you be a bit clearer?

In any case, Paired t test is appropriate for comparing pre- and post- technology use.

I am doing a comparative study of certain Key performance indicators (KPIs) in the organizations which

To compare 2 groups [those who are using technology vs. those who don’t use technology] on KPIs {if the measurement for KPIs is on a continuous scale and normally distributed}, and you have adequate sample in the 2 groups, then you can use Independent sample ttest.

If the above assumptions are violated, then you can use the non parametric test, which is Mann Whitney test.

If you have two measurements of the same variables on the same group (pre- and post-), then you can use a paired comparison t-test.

I agree with Dr. Muayyad Ahmad and Dr. David L Morgan

This link may help you

WHAT STATISTICAL ANALYSIS SHOULD I USE? STATISTICAL ANALYSES USING SPSS

https://stats.idre.ucla.edu/spss/whatstat/what-statistical-analysis-should-i-usestatistical-analyses-using-spss/

https://stats.idre.ucla.edu/spss/

In this link you will find

SPSS

• Classes and Seminars
• Learning Modules
• Frequently Asked Questions

Important Links

• Where to run SPSS? How to get SPSS?
• Installing, Customizing, Updating SPSS

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CHOOSING THE CORRECT STATISTICAL TEST IN SAS, STATA, SPSS AND R

https://stats.idre.ucla.edu/other/mult-pkg/whatstat/

Basic statistical tools in research and data analysis

Article Basic statistical tools in research and data analysis

Nonparametric statistical tests for the continuous data: the basic concept and the practical use

Article Nonparametric statistical tests for the continuous data: The...

BASIC STATISTICAL TOOLS

http://www.fao.org/docrep/W7295E/w7295e08.htm

Quantitative Data Analysis: Choosing a statistical test

To help choose which type of quantitative data analysis to use either before or after data has been collected. Before beginning this step in the research process, it is important to know the following information about the project:

· What is/are your specific research question(s)?

· What types of data will you collect -- nominal, ordinal, or ratio?

· What is/are the projected size(s) of your sample and groups?

· What are your independent and dependent variables?

https://www.uwstout.edu/parq/intranet/upload/what_quant_test_to_use.pdf

Lessons in biostatistics:

Comparing groups for statistical differences: how to choose the right statistical test?

http://www.biochemia-medica.com/content/comparing-groups-statistical-differences-how-choose-right-statistical-test

Choosing the right statistical test may at times, be a very challenging task for a beginner in the field of biostatistics.

This article will present a step by step guide about the test selection process used to compare two or more groups for statistical differences. We will need to know, for example, the type (nominal, ordinal, interval/ratio) of data we have, how the data are organized, how many sample/groups we have to deal with and if they are paired or unpaired. Also, we have to ask ourselves if the data are drawn from a Gaussian on non-Gaussian population. A key question is, if the proper conditions are met, should a one-tailed test or two-tailed test be used, the latter typically being the most powerful choice.

The appropriate approach is presented in a Q/A (Question/Answer) manner to provide to the user an easier understanding of the basic concepts necessary to fulfill this task. Some of the necessary fundamental concepts are: statistical inference, statistical hypothesis tests, the steps required to apply a statistical test, parametric versus nonparametric tests, one tailed versus two tailed tests etc.

In the final part of the article, a test selection algorithm will be proposed, based on a proper statistical decision-tree for the statistical comparison of one, two or more groups, for the purpose of demonstrating the practical application of the fundamental concepts.

Some much disputed concepts will remain to be discussed in other future articles, such as outliers and their influence in statistical analysis, the impact of missing data and so on.

The Statistics Tutor’s Quick Guide to Commonly Used Statistical Tests

http://www.statstutor.ac.uk/resources/uploaded/tutorsquickguidetostatistics.pdf

How to choose the right statistical test?

Article How to choose the right statistical test?

http://www.biostathandbook.com/testchoice.html

Handbook of Biological Statistics

John H. McDonald

Choosing a statistical test

Ten Common Statistical Errors and How to Avoid Them

https://www.nature.com/articles/ajg20085055

Handbook of Biological Statistics John H. McDonald (2014)

Good Luck

Thank you everyone for the answers.

@Dr. Muayyad Ahmad, the technology is new, hence the number of respondents will be very low and the data on KPIs will be obtained from the organizations, so not sure if it will be continuous and normally distributed. In that case I think non-parametric test would work best.

Non-parametric tests are used when you do not have interval data for dependent variable. they have nothing to do with the N available. But if you do have a small N, it will it difficult to detect a difference due to "low power" regardless of which test you use.

If you are counting the number of technologies used, and end up with a count variable (not continuous), then you should be using poisson regression, either as a repeated measures poisson regression with time as a predictor, or if two independent samples, then with organization (technology versus no technology) as your predictor. This would also be helpful if you have additional predictors (covariates) that you want to include, since the Mann Whitney Test would not allow you to do that.

"Mann Whitney U Test" helps to compare two different groups' data. In your case, if you took data from group of people before applying technology and after after technology, then you easily can check the significance of difference by comparing two groups through Mann Whitney U test in SPSS.

Good luck!

Paired sample t test