Chapter36b

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Research Methods in Education 6th Edition

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Chapter36b

  1. 1. INFERENTIAL STATISTICS © LOUIS COHEN, LAWRENCE MANION & KEITH MORRISON
  2. 2. STRUCTURE OF THE CHAPTER • Measures of difference between groups • The t-test (a difference test for parametric data) • Analysis of variance (a difference test for parametric data) • The chi-square test (a difference test and a test of goodness of fit for non-parametric data) • Degrees of freedom (a statistic that is used in calculating statistical significance in considering difference tests) • The Mann-Whitney and Wilcoxon tests (difference tests for non-parametric data)
  3. 3. STRUCTURE OF THE CHAPTER • The Kruskal-Wallis and Friedman tests (difference tests for non-parametric data) • Regression analysis (prediction tests for parametric data) • Simple linear regression (predicting the value of one variable from the known value of another variable) • Multiple regression (calculating the different weightings of independent variables on a dependent variable) • Standardized scores (used in calculating regressions and comparing sets of data with different means and standard deviations)
  4. 4. REGRESSION • Regression is a statistical technique of modelling the relationship between variables. • From knowing the values of one variable we can predict the values of another variable
  5. 5. SIMPLE LINEAR REGRESSION • Simple linear regression – the model includes one explanatory variable (independent) and one explained variable (dependent) – The relationship between examinations and stress
  6. 6. A SIMPLE REGRESSION Score on final university examination 100908070605040 Hoursperweekonprivatestudy 90 80 70 60 50 40 30
  7. 7. A SIMPLE REGRESSION (SPSS) Model Summary .966a .932 .932 3.000 Model 1 R R Square Adjusted R Square Std. Error of the Estimate Predictors: (Constant), Hours per week on private studya. ANOVAb 12154.483 1 12154.483 1350.089 .000a 882.267 98 9.003 13036.750 99 Regression Residual Total Model 1 Sum of Squares df Mean Square F Sig. Predictors: (Constant), Hours per week on private studya. Dependent Variable: Score on final university examinationb.
  8. 8. A SIMPLE REGRESSION Coefficientsa 22.201 1.432 15.504 .000 .763 .021 .966 36.744 .000 (Constant) Hours per week on private study Model 1 B Std. Error Unstandardized Coefficients Beta Standardized Coefficients t Sig. Dependent Variable: Score on final university examinationa. The beta weighting (ß) is ‘the amount of standard deviation unit of change in the dependent variable for each standard deviation unit of change in the independent variable’. Here the standardized beta weighting is .966, i.e. it is highly statistically significant (ρ=0.000 in the ‘Sig.’ column); this means that for every standard deviation unit change in the independent variable (‘hours per week on private study’) there is .966 of a unit rise in the dependent variable (‘score on final university examination’) i.e. there is nearly a one-to-one correspondence.
  9. 9. 10 MULTIPLE LINEAR REGRESSION • The model is a linear equation with at least two explanatory variables (independent) and one explained variable (dependent)
  10. 10. USING MULTIPLE REGRESSION • Multiple regression is useful in calculating the relative weighting of two or more independent variables on a dependent variable. Using the beta (β) weighting, multiple regression calculates how many standard deviation units are changed in the dependent variable for each standard deviation unit of change in each of the independent variables. • For example, let us say that we wished to investigate the relative weighting of ‘hours per week of private study’ and ‘motivation level’ as independent variables acting on the dependent variable ‘score on final university examination’.
  11. 11. Hours of study per week Level of motivation Final examination score
  12. 12. USING MULTIPLE REGRESSION (SPSS) Model Summary .969a .939 .938 2.852 Model 1 R R Square Adjusted R Square Std. Error of the Estimate Predictors: (Constant), Motivation level, Hours per week on private study a. The Adjusted R square is .938, i.e. the amount of the dependent variable explained by the two independent variables is very high (93.8%).
  13. 13. USING MULTIPLE REGRESSION ANOVAb 12247.676 2 6123.838 752.796 .000a 789.074 97 8.135 13036.750 99 Regression Residual Total Model 1 Sum of Squares df Mean Square F Sig. Predictors: (Constant), Motivation level, Hours per week on private studya. Dependent Variable: Score on final university examinationb. The analysis of variance (ANOVA) is highly statistically significant (ρ=.000), i.e. the relationship between the independent variable and the dependent variable is very strong.
  14. 14. USING MULTIPLE REGRESSION (SPSS) Coefficientsa 22.577 1.366 16.531 .000 .714 .024 .904 29.286 .000 .404 .119 .104 3.385 .001 (Constant) Hours per week on private study Motivation level Model 1 B Std. Error Unstandardized Coefficients Beta Standardized Coefficients t Sig. Dependent Variable: Score on final university examinationa.
  15. 15. USING MULTIPLE REGRESSION The independent variable ‘hours per week of private study’ has the strongest positive predictive power (β=.904) on the dependent variable ‘score on final university examination’, and this is statistically significant (the column ‘Sig.’ indicates that the level of significance, at .000, is stronger than .001); The independent variable ‘motivation level’ has strong positive predictive power (β=.104) on the dependent variable ‘score on final university examination’, and this is statistically significant (the column ‘Sig.’ indicates that the level of significance, at .001); Though both independent variables have a statistically significant weighting on the dependent variable, the beta weighting of the independent variable ‘hours per week of private study’ (β=.904) is much higher than that of the independent variable ‘motivation level’ (β=.104) on the dependent variable ‘score on final university examination’, i.e. ‘hours per week on private study’ is a stronger predictor of ‘score on final university examination’ than ‘motivation level’.
  16. 16. USING MULTIPLE REGRESSION The researcher can predict that, if the hours per week spent in private study were known, and if the motivation level of the student was known, then the likely score on the final university examination could be predicted. The formula would be: ‘Score on final university examination’ = (β x ‘hours per week on private study’) + (β x ‘motivation level’) In the example, the β for ‘hours per week on private study’ is 0.904, and the β for ‘motivation level’ is 0.104. These are the relative weightings of the two independent variables. So, for example, for a student who spends 60 hours per week on private study and has a high motivation level (9) the formula becomes: ‘Score on final university examination’ = (0.904 x 60) + (0.104 x 9) = 54.24 + 0.936 = 55.176
  17. 17. For example, if the beta weighting were different, and for different factors, the relationship between examination mark, study time and intelligence could be: Examination mark = β Study time + β Intelligence Examination mark = 0.65 Study time + 0.30 Intelligence A student with an intelligence score of 110 who studies for 30 hours per week will obtain the following examination mark: Examination mark = (0.65 x 30) + (0.30 x 110) = 19.5 + 33 = 52.5 If the same student studies for 40 hours then: Examination mark = (0.65 x 40) + (0.30) x 110 = 26 + 33 = 59
  18. 18. Beta Coefficients .323 .000 .080 .000 .205 .000 .116 .000 .087 .000 .076 .000 .067 .000 .044 .000 .157 .000 .100 .000 .071 .000 .058 .000 .164 .000 .092 .000 Teacher voice and support Workload Benefits and rewards of teaching Managing students Challenge and debate Family pressures Considering leaving teaching Emotions and coping Burnout Balancing work, family and cultural expectations Local culture Stress from family Stress reproducing stress Control and relationships Beta Standardized Coefficients Significance level STRESS IN TEACHING (SPSS): BETA WEIGHTINGS OF VARIABLES
  19. 19. Beta Coefficients .316 .000 .096 .000 .219 .000 .114 .000 .099 .000 .102 .000 .091 .000 .156 .000 .131 .000 .162 .000 .130 .000 Teacher voice and support Workload Benefits and rewards of teaching Managing students Challenge and debate Considering leaving teaching Emotions and coping Burnout Local culture Stress reproducing stress Control and relationships Beta Standardized Coefficients Significance level STRESS IN TEACHING (SPSS) (removing variables affects beta weightings)
  20. 20. COLLINEARITY DIAGNOSTICS (MULTI-COLLINEARITY) • Multicollinearity: The correlation between each independent variable should not be too high. Collinearity diagnostics indicates the level of correlation. If the collinearity is too high between two variables then it may be advisable to remove one. • Multi-collinearity is tested by (a) Tolerance and (b) the Variance Inflation Factor.
  21. 21. COLLINEARITY DIAGNOSTICS (MULTI-COLLINEARITY) Tolerance: ‘An indicator of how much of the variability of the specified independent is not explained by the other independent variables in the model . . . . If this value is very small (less than .10), it indicates that the multiple correlation with other variables is high, suggesting the possibility of collinearity’ (Pallant, 2007: 156). ‘The VIF (Variance Inflation Factor), which is just the inverse of the Tolerance Factor (1 divided by Tolerance). VIF values above 10 would be a concern here, indicating multicollinearity’ (Pallant, 2007: 156).
  22. 22. COLLINEARITY DIAGNOSTICS (SPSS) (MULTI-COLLINEARITY) Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF 1 (Constant) 22.577 1.366 16.531 .000 Hours per week on private study .714 .024 .904 29.286 .000 .655 1.528 Motivation level .404 .119 .104 3.385 .001 .655 1.528 a. Dependent Variable: Score on final university examination
  23. 23. COLLINEARITY DIAGNOSTICS (MULTI-COLLINEARITY) In the example: Hours per week on private study: Tolerance = .655 VIF = 1.528 Motivation level Tolerance = .655 VIF = 1.528 There is no problem with multicollinearity.
  24. 24. SOME ASSUMPTIONS IN REGRESSION • Random sampling; • Ratio data; • The removal of outliers: check outliers by calculating the Mahalanobis distance (in SPSS) ; • The supposed linearity of the measures is justifiable; • Interaction effects of independent variables (in non-recursive models) are measured; • The selection for the inclusion and exclusion of variables is justifiable; • The dependent variable and the residuals (the distance of the cases from the line of best fit) is approximately normally distributed;
  25. 25. SOME ASSUMPTIONS IN REGRESSION • The variance of each variable is consistent across the range of values for all other variables (or at least the next assumption is true); • The independent variables are approximately normally distributed, the variation is even across the levels/values of the variable (homoscedasticity). • Collinearity/multicollinearity is avoided. • Regressions are only as robust as the variables included, and the inclusion or removal of one or more independent variables affects their relative weightings on the dependent variable.
  26. 26. STEPWISE REGRESSION • To find a model with predictive accuracy, working with a limited number of independent variables from a longer list of independent variables, to determine which ones have a statistically significant influence on the dependent variables. • Stepwise multiple regression enters variables one at a time, in a sequence, to see which adds to the explanatory power of a model, by looking at its impact on the R-squared – whether it increases the R-square value.
  27. 27. STEPWISE REGRESSION Stepwise multiple regression enables the researcher to see which variables have predictive power and which do not, which to include and which to exclude in an explanatory model.
  28. 28. LOGISTIC REGRESSION • To enable the researcher to work with categorical variables in a multiple regression where the dependent variable is a categorical variable. • The independent variables may be categorical, discrete or continuous.
  29. 29. PROCEDURE FOR MULTIPLE REGRESSION 1. Analyze → Regression → Linear. 2. Send over dependent variable to Dependent box. 3. Send over independent variables to Independent box. 4. Click on Statistics. Tick the boxes Estimates, Confidence Intervals, Model fit, Descriptives, Part and partial correlations, Collinearity diagnostics, Casewise diagnostics and Outliers outside 3 standard deviations. Click Continue. 5. Click on Options. Click on Exclude cases pairwise. Click Continue. 6. Click on Plots. Send over *ZRESID to the Y box. Send over *ZPRED to the X box. Click on Normal probability plots. Click Continue. 7. Click on Save. Click the Mahalanobis box and the Cook’s box. Click Continue. 8. Click OK.
  30. 30. EXAMINING MULTIPLE REGRESSION SPSS OUTPUT 1. Check collinearity statistics: a. Tolerance must be higher than .10; b. VIF (Variance Inflation Factor) must not be higher than 10. 2. Check normality, linearity and homoscedasticity: a. Normality Probability Plot (Normal P-P Plot) to have points going in a straight diagonal line, bottom left to top right; b. Scatterplot to be a rectangle with scores concentrated in the centre (along the 0 point), avoiding curvilinear or uneven distribution. 3. Check that the Cook’s Distance maximum value is below 1 and that the Mahal. Distance is lower than the critical value.
  31. 31. EXAMINING MULTIPLE REGRESSION SPSS OUTPUT 4. Check the Adjusted R Square. 5. Check ANOVA and its significance level. 6. Check the Standardized Beta Coefficients and their significance levels. 7. Square each Parts correlation coefficient to see the contribution of each variable to the total Adjusted R Square (i.e. how much of the total variance in the dependent variable is explained by each independent variable).
  32. 32. COLLINEARITY DIAGNOSTICS (MULTI-COLLINEARITY) 1. Analyze → 2. Regression → 3. Linear → 4. Statistics → 5. Click ‘Collinearity diagnostics’ 6. Click ‘Continue’ 7. Continue with the multiple regression.
  33. 33. PROCEDURES FOR STEPWISE MULTIPLE REGRESSION IN SPSS • ‘Analyze’ → ‘Regression ‘→ ‘Linear ‘→ Enter dependent and independent variables → In the ‘Method’ box, change ‘Enter’ to ‘Stepwise’ → Click ‘OK’.
  34. 34. PROCEDURES FOR LOGISTIC REGRESSION IN SPSS Analyze → Regression → Binary Logistic → Insert dependent variable in the ‘Dependent’ box → Insert independent variables into the ‘Covariates’ box → Click on ‘Categorical’ → Move first categorical variable into the ‘Categorical Covariates’ box → Click the radio button ‘First’ → Click the ‘Change’ button → Repeat this for every categorical variable → Click ‘Continue’ to return to the first screen → Click ‘Options’ → Click the boxes ‘Classification plots’, Hosmer-Lemeshow goodness of fit’, ‘Casewise listing of residuals’ and ‘CI for Exp(B) → Click ‘Continue’ to return to first screen → Click ‘OK’.
  35. 35. THE NEED FOR A STANDARDIZED SCORE • A child tells his parents that he scored a mark of 75 for a maths test; his parents scold him. • A child tells his parents that he scored a mark of 2 for a history test; his parents praise him. • A child tells his parents that he scored a mark of 25 for an English test and a mark of 60 for a Physics test; his parents praise him for both. • A child tells his parents that he scored a mark of 80 for a Geography test and a mark of 120 for a Chemistry test; his parents scold him for both.
  36. 36. THE NEED FOR A STANDARDIZED SCORE • We need to know how to judge whether a mark is high or low. • We need to be able to compare marks between one test and another. • Therefore we need to know the scale of the marks, the range of the marks, the mean of the marks, and the distribution of the marks either side of the mean.
  37. 37. THE NEED FOR A STANDARDIZED SCORE • We need to know how to compare marks from a test which: – uses one scale with marks from a test which uses another scale; – has one range of marks with marks from a test that has another range of marks; – has a mean which is different from the mean of another test; – Has a distribution around the mean which is different from the distribution of another test.
  38. 38. THE Z-SCORE (STANDARDIZED SCORE) • Standardizing scores lets us judge whether a mark is high or low. • Standardizing scores lets us compare marks between one test and another when two different tests have different scales, range, means and distributions around the mean.
  39. 39. Z-SCORES • z-scores have the same mean and standard deviation, even though the original sets of scores had different means and standard deviations, i.e. z-scores let you compare fairly. • A z-score tells us how many standard deviations someone’s scores are above or below the mean.
  40. 40. Z-SCORES • To calculate the z-score subtract the mean from the raw score and divide that answer by the standard deviation. • For example if the raw score is 15, the mean is 10, and the standard deviation is 4, then 15-10 = 5 and 5 ÷ 4 = 1.25. • Here z-score tells us that the person’s score is 1.25 standard deviations above the mean. Is that score good or bad? How good or bad is it? s xx e sampletheofmeanthescoreactualthe z i − = − = sampltheofdeviationstandard
  41. 41. The mean One standard deviation either side of the mean accounts for 68.3% of the population. Two standard deviations either side of the mean accounts for 95.4% of the population.
  42. 42. STANDARDIZED SCORE (Z-SCORE) • A z-score of +1.4 indicates that someone is 1.4 standard deviations above the mean. • A z-score of -1.4 indicates that someone is 1.4 standard deviations below the mean. • If the z-score is positive, it indicates that the value is above the mean. • If the z-score is negative, it means that the value is below the mean. Is that z-score good or bad? How good or bad is it? We need to know about the probability of a certain value falling into a certain range of value.
  43. 43. 68% of the population lie between -1 and +1 standard deviations 95% of the population lie between -2 and +2 standard deviations 99% of the population lie between -1 and +1 standard deviations The normal curve lets us interpret the probability of a score falling into a certain range of scores/values.
  44. 44. The normal curve lets us interpret the probability of a score falling into a certain range of scores/values.
  45. 45. Let us say that, for 1200 people: The mean = 35 The standard deviation = 13 If a member of the group says he is 61 years old then it is clear that this person is much older than the average. But how much older? To be exact, we can convert his score into a z-score. 2 13 3561 = − =z
  46. 46. • This tells us that 61 is 2 standard deviations above the mean. • Refer to the ‘areas under the standard normal curve’ table (in statistics textbooks), for z = 2, the ‘area under curve beyond one point’ is 0.023. • The proportion of people that are 61 years of age or more is only 2.3% of the total. 2 13 3561 = − =z
  47. 47. For example if z-score is 1.25. Is that score good or bad? How good or bad is it? • Refer to the ‘areas under the standard normal curve’ table (in statistics textbooks), for z = ±1.25, the ‘area under curve beyond one point’ is 0.1056. • The proportion of people who score 1.25 or more is only 10.56% of the total. So, the score is very good.
  48. 48. An online calculator of area under the curve for standardized scores is at: www.danielsoper.com/statcalc/calc02.aspx This calculator gives the cumulative area under the curve (a figure as a decimal fraction that is less than 1 (let us call it X). To find the area under the curve beyond that one point simply subtract this figure from 1 (the formula, then is 1-X).
  49. 49. CALCULATING Z-SCORES WITH SPSS • Click on ‘Analyze’ → ‘Descriptive Statistics’ → Descriptives’. • Send over the variables to ‘Variables’ → ‘Click the box ‘Save standardized values as variables’ → Click ‘OK’→ Two new variables will be created.
  50. 50. T-SCORES • Some people are uncomfortable with z-scores, as they don’t like negative scores and they do not like an average being 0. • To overcome this, z-scores can be converted to T- scores. To convert a z-score to a T-score, multiply the z-score by 10 and add 50 to the result. • For example a z-score of .5, multiplied by 10 gives 5, and then, with 50 added, gives 55. The T-score is 55. • Many IQ tests and standardized tests convert z- scores. For example a common conversion in IQ tests is to multiply the z-score by 15 and add 100. So a z-score on an IQ test might be .5, multiplied by 15 gives 7.5, with 100 added gives 107.5, i.e. the IQ z-score converts to a T-score of 107.5.
  51. 51. THE MCCALL T-SCORE • The McCall T-score has a mean of 50 and a standard deviation of 10: – McCall T-score = 50±(z-score x 10) • For the ± sign, the part in brackets should be added to the 50 if the z-score is positive (i.e. if the raw score is above the mean) and subtracted if the z-score is negative (i.e. if the raw score is below the mean).

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