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Quantitative Academic Research
Supervision
Questionnaire design
Research Methodology
Data Analysis using SPSS
Bachelor's, Master's and PhD degrees
Professional assistance in Academic and Market Research
Distinguish yourself from your peers and/or competitors by judiciously choosing a systematic and scientific data-based approach to problem-solving. Useful qualitative techniques like focus group discussions and in-depth interviews often reveal their limitations when it comes to meet research objectives and bring solutions to research problems. In particular, exploratory, explanatory and causal research, which call for manipulation of numerical data, also require that hypotheses be tested. These are the main features included in the Quantitative Research Packages offered to you:
Questionnaire design
Tips on writing up of your methodology in line with your research objectives/needs
Data processing in SPSS and interpretation of results
Guidance on writing up of recommendations
I also propose to supervise your academic research and provide you guidance as to how to remain focused on your research objectives. I meticulously choose the best methodology for you to conclude your research project successfully. Above all, I always aim to get you a distinction in your dissertation.
08/10/2026
28/09/2026
"๐ ๐ฅ๐ฐ๐ฏ'๐ต ๐ซ๐ถ๐ด๐ต ๐ญ๐ฐ๐ฐ๐ฌ ๐ข๐ต ๐ฏ๐ถ๐ฎ๐ฃ๐ฆ๐ณ๐ด; ๐ ๐ง๐ช๐ฏ๐ฅ ๐ต๐ฉ๐ฆ ๐ฏ๐ข๐ณ๐ณ๐ข๐ต๐ช๐ท๐ฆ ๐ฉ๐ช๐ฅ๐ฅ๐ฆ๐ฏ ๐ช๐ฏ๐ด๐ช๐ฅ๐ฆ ๐ต๐ฉ๐ฆ๐ฎ."
๐ฟ๐ง. ๐๐๐๐๐จ๐ ๐๐ช๐ฃ๐๐จ๐
PhD (Statistics)
03/08/2026
๐ฆ๐๐ฎ๐๐๐ผ๐ป๐๐๐น
Your one-stop shop for
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๐ฃ๐ผ๐๐๐ฎ๐น ๐ฎ๐ฑ๐ฑ๐ฟ๐ฒ๐๐: 15 Cantons No.2, Vacoas
03/08/2026
๐๐ ๐ฃ๐๐ฅ๐ ๐ฆ๐ง๐๐ง๐ฆ ๐๐จ๐๐๐๐๐ก๐
Building your own little "empire" starts with ๐๐ต๐ฟ๐ฒ๐ฒ key steps:
1. Taking full personal responsibility
2. Focusing your time on high-value actions, and
3. Launching a small, independent project.
๐ ๐ ๐ถ๐ป๐ฑ๐๐ฒ๐ ๐ฎ๐ป๐ฑ ๐๐ผ๐ฐ๐๐
โ ๐๐ฌ๐ฃ ๐ฎ๐ค๐ช๐ง ๐ก๐๐๐
๐๐ต๐ฐ๐ฑ ๐ฎ๐ข๐ฌ๐ช๐ฏ๐จ ๐ฆ๐น๐ค๐ถ๐ด๐ฆ๐ด ๐ข๐ฏ๐ฅ ๐ข๐ค๐ค๐ฆ๐ฑ๐ต ๐ต๐ฉ๐ข๐ต ๐ฆ๐ท๐ฆ๐ณ๐บ ๐ด๐ฎ๐ข๐ญ๐ญ ๐ฅ๐ข๐ช๐ญ๐บ ๐ค๐ฉ๐ฐ๐ช๐ค๐ฆ ๐ฆ๐ช๐ต๐ฉ๐ฆ๐ณ ๐ฃ๐ถ๐ช๐ญ๐ฅ๐ด ๐ฐ๐ณ ๐ฃ๐ณ๐ฆ๐ข๐ฌ๐ด ๐บ๐ฐ๐ถ๐ณ ๐ง๐ถ๐ต๐ถ๐ณ๐ฆ.
โ ๐๐จ๐ ๐ฉ๐๐ ๐ก๐๐ฌ ๐ค๐ ๐ฉ๐ฌ๐ค ๐๐ค๐ช๐ง๐จ
๐๐ฑ๐ฆ๐ฏ๐ฅ ๐ต๐ธ๐ฐ ๐ฉ๐ฐ๐ถ๐ณ๐ด ๐ฐ๐ง ๐ฅ๐ฆ๐ฆ๐ฑ, ๐ง๐ฐ๐ค๐ถ๐ด๐ฆ๐ฅ ๐ธ๐ฐ๐ณ๐ฌ ๐ฐ๐ฏ ๐บ๐ฐ๐ถ๐ณ ๐จ๐ฐ๐ข๐ญ ๐ช๐ฏ๐ด๐ต๐ฆ๐ข๐ฅ ๐ฐ๐ง ๐ฆ๐ช๐จ๐ฉ๐ต ๐ฅ๐ช๐ด๐ต๐ณ๐ข๐ค๐ต๐ฆ๐ฅ ๐ฉ๐ฐ๐ถ๐ณ๐ด.
โ ๐๐ง๐ค๐ฉ๐๐๐ฉ ๐ฎ๐ค๐ช๐ง ๐๐ฃ๐๐ง๐๐ฎ
๐๐ข๐บ ๐ฏ๐ฐ ๐ต๐ฐ ๐ฅ๐ช๐ด๐ต๐ณ๐ข๐ค๐ต๐ช๐ฐ๐ฏ๐ด ๐ข๐ฏ๐ฅ ๐ฌ๐ฆ๐ฆ๐ฑ ๐บ๐ฐ๐ถ๐ณ ๐ฅ๐ข๐ช๐ญ๐บ ๐ณ๐ฐ๐ถ๐ต๐ช๐ฏ๐ฆ ๐ด๐ช๐ฎ๐ฑ๐ญ๐ฆ ๐ข๐ฏ๐ฅ ๐ฒ๐ถ๐ช๐ฆ๐ต.
๐ ๐ง๐ฎ๐ธ๐ถ๐ป๐ด ๐๐ฐ๐๐ถ๐ผ๐ป
โ ๐๐ช๐ง๐ฃ ๐ฎ๐ค๐ช๐ง ๐ฅ๐๐จ๐จ๐๐ค๐ฃ ๐๐ฃ๐ฉ๐ค ๐ฅ๐ง๐ค๐๐๐จ๐จ๐๐ค๐ฃ
๐๐ช๐ฏ๐ฅ ๐ธ๐ฉ๐ข๐ต ๐ง๐ฆ๐ฆ๐ญ๐ด ๐ญ๐ช๐ฌ๐ฆ ๐ฑ๐ญ๐ข๐บ ๐ต๐ฐ ๐บ๐ฐ๐ถ, ๐ฃ๐ถ๐ต ๐ญ๐ฐ๐ฐ๐ฌ๐ด ๐ญ๐ช๐ฌ๐ฆ ๐ธ๐ฐ๐ณ๐ฌ ๐ต๐ฐ ๐ฐ๐ต๐ฉ๐ฆ๐ณ๐ด. ๐ ๐ฐ๐ถ ๐ข๐ณ๐ฆ ๐จ๐ฐ๐ช๐ฏ๐จ ๐ต๐ฐ ๐ข๐ถ๐ต๐ฐ๐ฎ๐ข๐ต๐ช๐ค๐ข๐ญ๐ญ๐บ ๐ฐ๐ถ๐ต๐ค๐ฐ๐ฎ๐ฑ๐ฆ๐ต๐ฆ ๐ต๐ฉ๐ฆ๐ฎ ๐ฃ๐ฆ๐ค๐ข๐ถ๐ด๐ฆ ๐บ๐ฐ๐ถ'๐ณ๐ฆ ๐ฅ๐ฐ๐ช๐ฏ๐จ ๐ช๐ต ๐ฆ๐ง๐ง๐ฐ๐ณ๐ต๐ญ๐ฆ๐ด๐ด๐ญ๐บ. ๐๐ฐ ๐บ๐ฐ๐ถ, ๐ช๐ต ๐ช๐ด ๐ข๐ณ๐ต, ๐ฃ๐ฆ๐ข๐ถ๐ต๐บ, ๐ซ๐ฐ๐บ, ๐ง๐ญ๐ฐ๐ธ ๐ข๐ฏ๐ฅ ๐ง๐ถ๐ญ๐ง๐ช๐ญ๐ญ๐ช๐ฏ๐จ.
โ ๐๐ฉ๐๐ง๐ฉ ๐จ๐ข๐๐ก๐ก
๐๐ฆ๐ฆ๐ฑ ๐บ๐ฐ๐ถ๐ณ ๐ณ๐ฆ๐จ๐ถ๐ญ๐ข๐ณ ๐ซ๐ฐ๐ฃ ๐ข๐ฏ๐ฅ ๐ฃ๐ถ๐ช๐ญ๐ฅ ๐บ๐ฐ๐ถ๐ณ ๐ฏ๐ฆ๐ธ ๐ฑ๐ณ๐ฐ๐ซ๐ฆ๐ค๐ต ๐ฐ๐ฏ ๐ต๐ฉ๐ฆ ๐ด๐ช๐ฅ๐ฆ.
โ ๐๐๐ก๐ก ๐จ๐ค๐ข๐๐ฉ๐๐๐ฃ๐ ๐๐๐จ๐ฉ
๐๐ณ๐ฆ๐ข๐ต๐ฆ ๐ข ๐ด๐ช๐ฎ๐ฑ๐ญ๐ฆ, ๐ฃ๐ถ๐ต ๐ข๐ถ๐ต๐ฉ๐ฆ๐ฏ๐ต๐ช๐ค ๐ข๐ฏ๐ฅ ๐ถ๐ฏ๐ช๐ฒ๐ถ๐ฆ, ๐ฑ๐ณ๐ฐ๐ฅ๐ถ๐ค๐ต ๐ฐ๐ณ ๐ด๐ฆ๐ณ๐ท๐ช๐ค๐ฆ ๐ฐ๐ฏ๐ญ๐ช๐ฏ๐ฆ ๐ข๐ฏ๐ฅ ๐ต๐ณ๐บ ๐ต๐ฐ ๐ฎ๐ข๐ฌ๐ฆ ๐บ๐ฐ๐ถ๐ณ ๐ง๐ช๐ณ๐ด๐ต ๐ด๐ข๐ญ๐ฆ ๐ต๐ฐ ๐ต๐ณ๐ข๐ช๐ฏ ๐บ๐ฐ๐ถ๐ณ ๐ฃ๐ณ๐ข๐ช๐ฏ ๐ต๐ฐ ๐ข๐ค๐ค๐ฆ๐ฑ๐ต ๐ฎ๐ฐ๐ฏ๐ฆ๐บ ๐ง๐ฐ๐ณ ๐บ๐ฐ๐ถ๐ณ ๐ท๐ข๐ญ๐ถ๐ฆ.
โ ๐๐ฌ๐ฃ ๐ฎ๐ค๐ช๐ง ๐๐ช๐๐๐๐ฃ๐๐
๐๐ฐ๐ฎ๐ฎ๐ถ๐ฏ๐ช๐ค๐ข๐ต๐ฆ ๐ฅ๐ช๐ณ๐ฆ๐ค๐ต๐ญ๐บ ๐ท๐ช๐ข ๐ฆ๐ฎ๐ข๐ช๐ญ ๐ฐ๐ณ ๐ฑ๐ฉ๐ฐ๐ฏ๐ฆ ๐ธ๐ช๐ต๐ฉ ๐บ๐ฐ๐ถ๐ณ ๐ค๐ญ๐ช๐ฆ๐ฏ๐ต๐ด, ๐ช๐ฏ๐ด๐ต๐ฆ๐ข๐ฅ ๐ฐ๐ง ๐ณ๐ฆ๐ญ๐บ๐ช๐ฏ๐จ ๐ฐ๐ฏ๐ญ๐บ ๐ฐ๐ฏ ๐ด๐ฐ๐ค๐ช๐ข๐ญ ๐ฎ๐ฆ๐ฅ๐ช๐ข ๐ข๐ฑ๐ฑ๐ด ๐ต๐ฉ๐ข๐ต ๐ค๐ข๐ฏ ๐ญ๐ช๐ฎ๐ช๐ต ๐บ๐ฐ๐ถ๐ณ ๐ด๐ค๐ฐ๐ฑ๐ฆ ๐ฐ๐ง ๐ข๐ค๐ต๐ช๐ฐ๐ฏ.
Always strive to be the best in your field of competency through continuous learning and development.
Most of all, never forget how you started ๐
22/07/2026
๐๐ฐ๐ฏ๐ง๐ฆ๐ณ๐ฆ๐ฏ๐ค๐ฆ ๐๐ข๐ฑ๐ฆ๐ณ ๐ฑ๐ณ๐ฆ๐ด๐ฆ๐ฏ๐ต๐ข๐ต๐ช๐ฐ๐ฏ (2021)
๐๐ซ๐ฃ๐๐ข๐ฅ๐๐ง๐ข๐ฅ๐ฌ ๐๐๐๐ง๐ข๐ฅ ๐๐ก๐๐๐ฌ๐ฆ๐๐ฆ
Exploratory Factor Analysis (๐๐๐) is a statistical technique used in data analysis and research to ๐ณ๐ฆ๐ฅ๐ถ๐ค๐ฆ a large number of observed variables (e.g., survey statements or items) into a smaller, unobserved set of ๐ถ๐ฏ๐ฅ๐ฆ๐ณ๐ญ๐บ๐ช๐ฏ๐จ factors, which are to be judiciously named as the "common denominators" of grouped items.
๐ฆ๐ฎ๐บ๐ฝ๐น๐ฒ ๐ฆ๐ถ๐๐ฒ ๐ฅ๐ฒ๐พ๐๐ถ๐ฟ๐ฒ๐บ๐ฒ๐ป๐๐
EFA requires a sufficiently large sample to produce stable correlation estimates and reliable factor loadings. General rules of thumb include:
โ ๐๐ฃ๐ด๐ฐ๐ญ๐ถ๐ต๐ฆ ๐๐ช๐ฏ๐ช๐ฎ๐ถ๐ฎ: At least 100 to 150 cases.
โ ๐๐ถ๐ฃ๐ซ๐ฆ๐ค๐ต-๐ต๐ฐ-๐๐ข๐ณ๐ช๐ข๐ฃ๐ญ๐ฆ ๐๐ข๐ต๐ช๐ฐ: Aim for a ratio of 5 to 10 participants per variable (item) being measured.
โ ๐๐ฑ๐ต๐ช๐ฎ๐ข๐ญ: 300 or more cases generally yield robust, reliable results.
๐๐ฎ๐๐ฎ ๐๐๐๐๐บ๐ฝ๐๐ถ๐ผ๐ป๐
Prior to unveiling latent factors, data assumptions must be tested to confirm the ๐ง๐ข๐ค๐ต๐ฐ๐ณ๐ข๐ฃ๐ช๐ญ๐ช๐ต๐บ of items:
โ The ๐ฐ๐ผ๐ฟ๐ฟ๐ฒ๐น๐ฎ๐๐ถ๐ผ๐ป ๐บ๐ฎ๐๐ฟ๐ถ๐
should display several correlations r โฅ 0.30
โ The diagonal elements of the ๐ฎ๐ป๐๐ถ-๐ถ๐บ๐ฎ๐ด๐ฒ ๐ฐ๐ผ๐ฟ๐ฟ๐ฒ๐น๐ฎ๐๐ถ๐ผ๐ป ๐บ๐ฎ๐๐ฟ๐ถ๐
should be greater than 0.5
โ The ๐๐ฎ๐ถ๐๐ฒ๐ฟ-๐ ๐ฒ๐๐ฒ๐ฟ-๐ข๐น๐ธ๐ถ๐ป (๐๐ ๐ข) ๐๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ for ๐ด๐ข๐ฎ๐ฑ๐ญ๐ฆ ๐ข๐ฅ๐ฆ๐ฒ๐ถ๐ข๐ค๐บ should be greater than 0.5 (ideally > 0.7)
โ ๐๐ฎ๐ฟ๐๐น๐ฒ๐๐'๐ ๐๐ฒ๐๐ ๐ผ๐ณ ๐ฆ๐ฝ๐ต๐ฒ๐ฟ๐ถ๐ฐ๐ถ๐๐ should be significant at the 5% level (p < 0.05)
โ All ๐ฐ๐ผ๐บ๐บ๐๐ป๐ฎ๐น๐ถ๐๐ถ๐ฒ๐ must be at least 0.4 (ideally > 0.6)
Other assumptions that may be checked include:
โ ๐ ๐๐น๐๐ถ๐ฐ๐ผ๐น๐น๐ถ๐ป๐ฒ๐ฎ๐ฟ๐ถ๐๐ ๐ฎ๐ป๐ฑ ๐ฆ๐ถ๐ป๐ด๐๐น๐ฎ๐ฟ๐ถ๐๐
โ ๐๐ถ๐ป๐ฒ๐ฎ๐ฟ๐ถ๐๐ ๐ฎ๐ป๐ฑ ๐ข๐๐๐น๐ถ๐ฒ๐ฟ๐
โ ๐ ๐๐น๐๐ถ๐๐ฎ๐ฟ๐ถ๐ฎ๐๐ฒ ๐ก๐ผ๐ฟ๐บ๐ฎ๐น๐ถ๐๐
๐ ๐ฒ๐๐ต๐ผ๐ฑ๐ ๐ผ๐ณ ๐๐
๐๐ฟ๐ฎ๐ฐ๐๐ถ๐ผ๐ป
In popular software like ๐๐๐ ๐๐๐๐ ๐๐ต๐ข๐ต๐ช๐ด๐ต๐ช๐ค๐ด, EFA is carried out using ๐๐ณ๐ช๐ฏ๐ค๐ช๐ฑ๐ข๐ญ ๐๐ฐ๐ฎ๐ฑ๐ฐ๐ฏ๐ฆ๐ฏ๐ต๐ด ๐๐ฏ๐ข๐ญ๐บ๐ด๐ช๐ด (๐ฃ๐๐), though ๐๐ณ๐ช๐ฏ๐ค๐ช๐ฑ๐ข๐ญ ๐๐น๐ช๐ด ๐๐ข๐ค๐ต๐ฐ๐ณ๐ช๐ฏ๐จ (๐ฃ๐๐) or ๐๐ข๐น๐ช๐ฎ๐ถ๐ฎ ๐๐ช๐ฌ๐ฆ๐ญ๐ช๐ฉ๐ฐ๐ฐ๐ฅ (๐ ๐) may also be used.
๐ฅ๐ผ๐๐ฎ๐๐ถ๐ผ๐ป ๐ผ๐ณ ๐๐ฎ๐ฐ๐๐ผ๐ฟ๐
While historically ๐ผ๐ฟ๐๐ต๐ผ๐ด๐ผ๐ป๐ฎ๐น methods (e.g., ๐๐ข๐ณ๐ช๐ฎ๐ข๐น) were heavily used due to simpler hand computations, modern statistical consensus overwhelmingly prefers ๐ผ๐ฏ๐น๐ถ๐พ๐๐ฒ rotation (e.g., ๐๐ณ๐ฐ๐ฎ๐ข๐น or ๐๐ช๐ณ๐ฆ๐ค๐ต ๐๐ฃ๐ญ๐ช๐ฎ๐ช๐ฏ), as the factor correlation matrix can simply be checked to see if the factors are actually related.
๐๐ฎ๐ฐ๐๐ผ๐ฟ ๐ฅ๐ฒ๐๐ฒ๐ป๐๐ถ๐ผ๐ป ๐๐ฟ๐ถ๐๐ฒ๐ฟ๐ถ๐ฎ
Lastly, factors may be extracted according to any of the following three methods:
1. ๐๐ข๐ช๐ด๐ฆ๐ณ'๐ด ๐ค๐ณ๐ช๐ต๐ฆ๐ณ๐ช๐ฐ๐ฏ (eigenvalues greater than 1)
2. Cattell's ๐ด๐ค๐ณ๐ฆ๐ฆ ๐ฑ๐ญ๐ฐ๐ต
3. ๐๐ข๐ณ๐ข๐ญ๐ญ๐ฆ๐ญ ๐ข๐ฏ๐ข๐ญ๐บ๐ด๐ช๐ด (using Monte Carlo simulation)
For more information, please contact me via email (๐ฟ๐ฎ๐ท.๐ด๐๐ป๐ฒ๐๐ต@๐ต๐ผ๐๐บ๐ฎ๐ถ๐น.๐ฐ๐ผ๐บ) or WhatsApp (+๐ฎ๐ฏ๐ฌ ๐ฑ๐ฐ๐ต๐ต ๐ต๐ฌ๐ณ๐ฌ).
Looking forward to welcoming you to our next workshop on IBM SPSS Statistics ๐
Date: Saturday 13 June 2026
Time: 09:30 - 13:30
Venue: Africa Learning Academy, Ebene
Learn how to master SPSS to analyse data. A workshop on
โ Questionnaire design and measurement scales
โ Basic and advanced SPSS functionalities
โ Data cleansing, coding and entry
โ Data testing: reliability, construct validity, normality
โ Descriptive statistics; method of weighted means
โ Correlation analysis
โ Multiple regression analysis
โ Association analysis
โ Exploratory factor analysis
โ Binary logistic regression
and more...
By Dr. Rajesh Gunesh
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