Because of Prof. S.S. Prasada Rao, I met with five high-level leaders yesterday on Zoom. What they really needed was some help with how to move forward with their study project. It's about figuring out what's wrong with the HR department of a well-known non-traditional energy company and how to fix it by suggesting a good plan. The HR department of the company is having some problems, such as functional teams working in silos, uncertainty about how to handle future growth, and the inability to move workers from one project to another. The company has very few people leaving.
I suggested the following steps.
1. Identify the functions that are plagued with issues.
2. Interview 20 to 30 employees from different departments and different levels. Collect their narratives. Identify the dominant issues from these narratives.
3. Survey many employees with a research questionnaire and collect data. The data will establish causal relationships among essential functions and the expected outcomes.
The research will help identify the dominant issues, measure the efficiency of essential functions, and their relationship with the primary outcomes.
Data Analysis for Ph.D. Using SPSS and AMOS
Let us understand how to analyse the data collected for PhD and also any other data.
04/12/2021
For your data analysis needs in connection with your PhD, please contact 7993909371 or email: [email protected]
Data Analysis for Ph.D. Using SPSS and AMOS Let us understand how to analyse the data collected for PhD and also any other data.
I am available for consulting on PhD in Social Sciences, Management, Commerce, Sociology, Social Work etc. WhatsApp 7993909371
Please contact me on my WhatsApp phone 7993909371 for your data analysis needs.
The Purpose of Factor Analysis
The purpose of Factor Analysis or Principal Components Analysis is misunderstood.
It does not show causal connections, like Regression Analysis does.
It is used for grouping a several related variables into one set. Thus, several variables are categorized under major variables. Variables are reduced. The number of variables is reduced from a bigger number to a smaller number.
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