1. SPSS RELATIONSHIP STATISTICS GUIDE FOR RESEARCH By PETER JAMES KPOLOVIE

2. Correlation Correlation is the statistical technique for determination of The magnitude and direction (positive or negative) Of relationships among variables in a research work. There is no psycho-social trait that is all by itself Without any atom of relationship with some other traits.

3. Correlation contd. Only when relevant data are collected on two variables For a truly representative sample and Correlated to obtain a coefficient of 0.00 or A coefficient that is not statistically significant, It cannot be conclusively said that the two variables are not related.

4. Correlation contd. Correlation uses a single value that ranges from 1 (for a perfect positive relationship) through 0 (for a total absence of relationship) to -1 (for a perfectly negative relationship) To explicitly represent the direction and magnitude To which two or more variables are interrelated.

5. Correlation Statistical Techniques. Frequently used Correlational Statistical Techniques in Research Four commonly used correlational statistical techniques for hypotheses testing in research are:  1)Pearson product moment correlation coefficient,  2)Partial correlation coefficient,  3) Regression,  4)Multiple Regression.

6. Example of Correlation. SubjectsEducation YearsSalary Grade Level Ati62 Bos2115 Cac179 Dit158 Esi168 Fig2014 Gim1810 Han135 Iro2316 Jam189

7. Data Entry and Saving.

8. ANALYZE. Analyze > Correlate > Bivariate> Transfer Variables > Options > Descriptive Statistics > OK.

9. Move Variables > Options > Means and Standard Deviations > OK: Output

10. Interpretation The correlation between YE and SGL of the Federal civil servants is 0.953. For two-tailed test, Sig. is.000, usually written or reported as.001. The Sig. of.001 is less than the chosen 0.05 alpha and even 0.01 alpha. Therefore, the null hypothesis of ‘no significant relationship between Federal civil servants’ years of education and salary grade level’ is rejected. Technically, [r (8) =.95, p <.05]; or [r (8) =.95, p <.01].

11. PARTIAL CORRELATION WITH SPSS: ENTER AND SAVE THE DATA UMECGPAIQ 200.003.22100.00 380.004.55230.00 215.003.0090.00 200.002.0075.00 250.003.50120.00 260.003.70115.00 222.001.99105.00 360.004.55235.00 230.002.50100.00 190.004.11190.00

12. Partial Correlation Statistical Operations. Analyze > Correlate > Partial > Transfer Variables > Options > Means and Standard deviations > Zero-Order Correlation > Continue > OK.

13. Analyze > Correlate > Partial

14. Move Variables > Options > Mean & SD > Zero-Order Correlation > Continue > OK

15. Output

16. Partial Correlation Output.

17. Interpretation Descriptive statistics shows the mean, standard deviation and number of cases. The top side of the second part of the results shows the zero-order correlations that UME and CGPA has an r of.853, UME and IQ has.968 r and CGPA and IQ has an r of.866; each with 8 df and is significant at.01 alpha.

18. Interpretation of Partial Correlation Output The bottom part shows the partial correlation coefficient or the first-order correlation between UME and CGPA to be.119, The df is 7, p value (Sig. 2-tailed) is.761 at alpha of 0.05. Therefore, the null hypothesis of no significant partial relationship between students’ UME and CGPA fails to be rejected as [r (7) =.119, p >.05]. Retain the null hypothesis.

19. RESEARCH TITLE  Please, do a Google search for, open, download, and study each of these following works.  1. MULTIPLE PREDICTION OF RESEARCH PRODUCTIVITY: H-INDEX.  2. CONTINENTAL INEQUITIES IN LIFE EXPECTANCY.  3. CONTINENTAL COMPARISON OF HUMAN DEVELOPMENT INDEX.

20. REGRESSION. Regression is a statistical technique of crucial importance that must be applied in any investigation that is directly aimed at prediction of the values of a criterion variable on the basis of known values the predictor. Whenever two or more independent variables are jointly used for the prediction of a dependent variable, it is referred to as multiple regression.

21. REGRESSION Contd. Regression is a statistical technique, Based on the principles of correlation, For analysis of linear relationship Between a predictor variable and one criterion variable In which the dependent variable value is equated with weighted value of the independent variable In addition to a constant term.

22. REGRESSION: Between R ehearsal Level and Memory. Rehearsal Memory 3.005.00 3.007.00 9.0020.00 8.0015.00 4.009.00 4.0010.00 5.0012.00 9.0017.00 6.0014.00 10.0018.00

23. Analyze > Regression > Linear

24. Move Variables > Statistics > Estimates > Model fit > Descriptives > Continue.

25. Save > Unstandardized Predicted Values > Unstandardized Residuals > OK.

26. Correlation of Memory and Rehearsal Level

27. Regression Model Summary

28. Regression ANOVA

29. Regression Coefficients

30. Interpretation  Descriptive Statistics (Mean and Standard Deviation) are given for both Variables.  The Model summary has indicated that the:  R is.956 and  R 2 is.915 (i.e., a coefficient of determination is 91.5%).

31. Interpretation Contd.  The Regression ANOVA sub-table has shown that:  Regression Sum of Squares is 201.301, df = 1, Mean Square = 201/302.  Residual Sum od Squares is 18.798, df = 8, Mean Square = 2.350.

32. Interpretation Contd.  The Regression F is 85.669; which is significant at.001 alpha.  Since the Sig. is smaller than the chosen alpha of.01, the null hypothesis of “Rehearsal Level does not significantly predict memory” is rejected.  Rehearsal Level significantly predicts memory.

33. Interpretation Contd. The Coefficients sub table has shown Unstandardized regression coefficient (B) of 1.761. It means for every 1 unit increase in rehearsal level, there is an increase of 1.761 in memory. The Standardized regression coefficient (  or Beta) is.956.

34. Interpretation Contd. The  or standardized regression coefficient of 0.956 is significant at even.001 as the p value (Sig.) is.000. Since the  =.956, p =.001, two-tailed, the null hypothesis of ‘no significant linear regression coefficient between Rehearsal and Memory’ is rejected. Knowledge of the independent variable (Rehearsal Level) values can be used to predict the values of the criterion variable (Memory) significantly better than 0 (zero).

35. MULTIPLE PREDICTION OF RESEARCH PRODUCTIVITY  CLOSELY TAKE A SECOND LOOK AT THE WORK YOU DOWNLOADED FROM VIA A SEARCH FOR:  Multiple-Prediction-of-Research- Productivity-H-Index.pdf (eajournals.org) Multiple-Prediction-of-Research- Productivity-H-Index.pdf (eajournals.org)  STUDY IT AND MASTER EVERY BIT OF RESEARCH EXECUTION THAT DEMANDS APPLICATION OF MULTIPLE REGRESSION AS ILLUSTRATED WITH THE MULTIPLE REGRESSION STATISTICAL TECHNIQUE.

36. MULTIPLE REGRESSION WITH SPSS CGPA VAR00001 WASC VAR00002 NECO VAR00003 UME VAR00004 PUME VAR00005 4654035 2523020 3534025 3.5435025 3.5323530 2.5143020 121 15 1.5212520 4463545 3342535

37. Analyze>Regression>Linear>Move Variables>Statistics>Descriptives>R squared change>Continue.

38. Linear Regression>Save>Unstandardized Predicted Values>Unstandardized Residual>Continue>OK.

39. Correlations Matrix

40. Model Summary.

41. Multiple Regression ANOVA.

42. Coefficients.

43. Interpretation The Descriptive Statistics (Mean and Standard deviation) of each of the Variables are provided in the first table. The Correlations table shows the entire pairwise correlation coefficients. It also indicates the actual significance level for each correlation coefficient.

44. Interpretation Contd. For example: CGPA and WASC has r of 0.54 (not significant p >.05). CGAP and NECO has r of 0.789 that is significant (p <.05). CGPA and UME has r of 0.739 which is significant (p <.05). CGPA and PUME has r of 0.837 that is significant (p <.05) for one-tailed test.

45. Model Summary. The third table, Model Summary, show: Multiple regression (R) of.975. Regression squared (R2) of.950. Adjusted R2 of.911. The standard error of estimate is.30867.

46. Change Statistics Change statistics in this table shows R 2 change of.950 which means that all the predictors (actually those that contributed significantly to the prediction) account for 95% of the variance in CGPA (the criterion). This R 2 is statistically significant (p <.05).

47. Tests of Significance for testing the Null Hypotheses The ANOVAs has shown that when converted to F: The multiple correlation has an F ratio of 23.940 Which is significant (p <.05).

48. Coefficients. Coefficients table, shows the unstandardized multiple regression of -.044 for Var00002,.069 for Var00003,.063 for Var00004 and.067 for Var00005. Most importantly are the standardized regression coefficients (Beta or β) that are actually tested for significance.

49. Coefficients Contd. The Beta for Var00002 is -.068 (not significant, p >.05),.111 for Var00003 (not significant, p >.05),.540 for Var00004 (significant, p <.05) and.598 for Var00005 (significant, p <.05).

50. Coefficients Contd. The variables that the 95% confidence intervals range cuts across zero, their contribution to the prediction of the criterion is not statistically significant (WASC and NECO). PUME and UME with lower and upper 95% confidence intervals fall completely above or completely below zero contributed significantly to the prediction of the criterion.

51. Coefficients Contd. The Multiple Regression Equation is written with the 1.Constant value and 2.Unstandardized Coefficient B values In the Coefficients table. Ŷ = a + b 1 X 1 + b 2 X 2 + b 3 X 3 + b 4 X 4. Ŷ = -1.146 + -.044(X 1 ) +.069(X 2 ) +.063(X 3 ) +.067(X 4 ).

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54. APPRECIATION A SEXTILLION THANKS FOR ACTIVELY PARTICIPATING IN THIS RESEARCH, DATA ANALYSIS AND INTERPRETATION COURSE. KPOLOVIE, PETER JAMES (SCIENTIST OF THE YEAR 2021) CONTACTS: 0916 050 0061 Email: kpolovie1@gmail.comkpolovie1@gmail.com Website: https://www.amazon.com/author/kpolovie.bookshttps://www.amazon.com/author/kpolovie.books