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MULTIPLE REGRESSION  A Journal Article Advanced Psychological Statistics Psy520M   Presented by:  FLORABEL S. SUAREZ
<ul><li>Steven K. Hedden,  University of Iowa </li></ul>Prediction of Music Achievement in the Elementary School Journal o...
The Research Problem The purpose of the present investigation was to evaluate a set of variables as predictors of music ac...
Description of Variables <ul><li>INDEPENDENT VARIABLES </li></ul><ul><li>DEPENDENT VARIABLE </li></ul>Academic achievement...
Appropriateness of the Analysis Number and type of variables: –  use of 2 or more predictor/IV variables of an interval/ra...
The Procedure T he study had chosen a group of fifth- and sixth-grade students from two schools who were attending regular...
Statistical Output
Statistical Output  –   Intercorrelations
Statistical Output  –   Intercorrelations
Statistical Output
Regression Analysis
Regression Analysis
Regression Analysis
Regression Analysis
Hypothesis: H 0 : R 2  = 0  H A : R 2  > 0 R 2  is estimated to be 33.64% (school one) R 2  is estimated to be 60.84% (sch...
Hypothesis:  What are the null and alternative hypothesis for testing whether the regression coefficient for music achieve...
Results of Analyses  For school one: Academic Achievement:  Attitude towards music:  Self-concept in music:  Music backgro...
Conclusion The major research question posed in the present investigation asked about the magnitude of the relationship be...
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Multiple regression journal (suarez)

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  • Find a journal article for the analysis assigned to you. Don’t expect to see raw data. Statistics presented are like those in the STATISTICA outputs, although reported according to APA-format. Means, correlations, regression coefficients (multiple regression) Test statistics and p-values ANOVA, chi-square, regression. Report all pertinent STATISTICS. State the result of the statistical test. Format: Powerpoint presentation (7-10 minutes) You can supplement the presentation by referring to a soft copy of the journal article. Most journal articles report more than one analyses. You do not report all of these analyses, but only the analysis assigned to you. The example must be for the analysis assigned to you; it shouldn’t be a complex variation of that analysis. For example, if you are assigned to do repeated-measures ANOVA: In class, we dealt only with repeated measures ANOVA having one independent variable. Do not choose an article where you have two independent variables both of which are repeated measures.
  • An elementary school music teacher who administers an achievement test to his or her students typically finds that some students demonstrate markedly greater achievement than others, even though all of the students have been exposed to the same materials and teaching strategies. Differences in music achievement among members of a given classroom apparently are attributable to factors other than teaching strategies or materials. What, then, are the more important factors that predict music achievement by elementary school students? Explain the research problem that the analysis aims to address. You are not to explain all the objectives of the research, but only that objective related to the analysis you are reporting. For this, refer to the introduction section of the article or the plan of analysis that is sometimes described at the start of the results section. State the null and alternative hypotheses.
  • What are the variables (dependent and independent) in the study and how are they measured? What are their levels of measurement? MAT was selected as a criterion measure based on its demonstrated reliability and validity, length, and suitability for elementary school students ITBS was administered on a regular basis at each of the elementary schools involved in the Study, administered at approximately the same time. Subjects&apos; ITBS scores were transcribed from school records. Gender was included as a predictor because there is an observable difference in maturity between boys and girls at the elementary school level. The manual for the Music Achievement Test (Colwell, 1969, p. 37) states that level of maturation is one of the factors that influences music achievement. The possibility that differences in music achievement are associated to some extent with gender deserved investigation.
  • To do this, you will have to discuss pertinent details in the method section. To do this, you will have to refer to the levels of measurement of your variables. For example, for multiple regression: restate that the criterion variables (aka IV) is on interval/ratio scale. In short, go through the data specifications for the analysis and show that these specifications are met by the data. The value of these multiple regression studies is that they provide information on the relative importance of certain variables with respect to music achievement. In a sense, these studies serve an exploratory function in that they investigate relationships rather than causal effects. That is, the results from such investigations may suggest future studies that would attempt to discover whether achievement might be enhanced by asking teachers to stress certain items as they provide instruction. For example, if it were found that attitude toward music and self-concept in music were significant predictors of music achievement, then a research study should investigate whether gains in music achievement could be facilitated by stressing music attitude and self-concept as instruction is provided.
  • An elementary school music teacher who administers an achievement test to his or her students typically finds that some students demonstrate markedly greater achievement than others, even though all of the students have been exposed to the same materials and teaching strategies. Differences in music achievement among members of a given classroom apparently are attributable to factors other than teaching strategies or materials. What, then, are the more important factors that predict music achievement by elementary school students? ITBS was administered on a regular basis at each of the elementary schools involved in the Study, administered at approximately the same time. Subjects&apos; ITBS scores were transcribed from school records. Explain the research problem that the analysis aims to address. You are not to explain all the objectives of the research, but only that objective related to the analysis you are reporting. For this, refer to the introduction section of the article or the plan of analysis that is sometimes described at the start of the results section. State the null and alternative hypotheses.
  • The first step in the analysis of data was the computation of various summary statistics. Table 1 provides a list of mean scores and standard deviations for the criterion variable and the four continuous predictor variables. These statistics are listed by school and for the total sample. The mean scores on the MAT attest to the instructional effectiveness of each music specialist. The MAT mean scores listed in Table 1 for schools one and two are equivalent to percentile ranks (sixth-grade norms) of 65 and 74, respectively. The differences among pairs of mean scores (particularly for the MAT and the SCIM) suggest that the scores from the two schools should not be combined for the multiple regression analysis. This was tested by conducting an analysis of variance on each of the variables listed in Table 1. A significant difference at the .05 level was detected between the SCIM mean scores from the two schools. The researcher then decided to conduct a separate regression analysis for each school.
  • Zero-order bivariate correlation coefficients (r) were computed as a preliminary step in the regression analyses. These coefficients are listed in Tables 2 and 3.
  • Zero-order bivariate correlation coefficients (r) were computed as a preliminary step in the regression analyses. These coefficients are listed in Tables 2 and 3.
  • Zero-order bivariate correlation coefficients (r) were computed as a preliminary step in the regression analyses. These coefficients are listed in Tables 2 and 3.
  • Table 4 lists the results of the multiple regression analysis at school one. All variables were added to the equation by the stepwise procedure that was employed, but there was a negligible increase in the size of R as the third through fifth variables were added. Inspection of Table 2 shows that the ITBS was the best predictor of the MAT and that the addition of the SCIM produced a moderate increase in predictive power. The major research question posed in the present investigation asked about the magnitude of the relationship between music achievement and a set of predictor variables-academic achievement, attitude toward music, self-concept in music, music background, and gender. Somewhat discrepant results were obtained at the two schools. At school one, a value of R = .58 was obtained by optimally weighting the two significant predictors (academic achievement and self-concept in music). At school two, a value of R = .78 was obtained when the two significant predictors (academic achievement and attitude toward music) were assigned optimal weights.
  • Table 4 lists the results of the multiple regression analysis at school one. All variables were added to the equation by the stepwise procedure that was employed, but there was a negligible increase in the size of R as the third through fifth variables were added. Inspection of Table 2 shows that the ITBS was the best predictor of the MAT and that the addition of the SCIM produced a moderate increase in predictive power. The major research question posed in the present investigation asked about the magnitude of the relationship between music achievement and a set of predictor variables-academic achievement, attitude toward music, self-concept in music, music background, and gender. Somewhat discrepant results were obtained at the two schools. At school one, a value of R = .58 was obtained by optimally weighting the two significant predictors (academic achievement and self-concept in music). At school two, a value of R = .78 was obtained when the two significant predictors (academic achievement and attitude toward music) were assigned optimal weights.
  • The results at school two are listed in Table 5 . Again, all variables were added to the equation, but only the first and second variables were significant predictors. The best single predictor of the MAT was the ITBS; the addition of the ATMS provided a moderate increase in predictive power.
  • The results at school two are listed in Table 5 . Again, all variables were added to the equation, but only the first and second variables were significant predictors. The best single predictor of the MAT was the ITBS; the addition of the ATMS provided a moderate increase in predictive power.
  • What are the null and alternative hypothesis for testing whether the regression coefficient for a predictor is significantly different from zero? H 0 : R 2 = 0 H A : R 2 &gt; 0 R 2 is estimated to be 68.109%
  • What are the null and alternative hypothesis for testing whether the regression coefficient for a predictor is significantly different from zero? H 0 : R 2 = 0 H A : R 2 &gt; 0
  • Transcript of "Multiple regression journal (suarez)"

    1. 1. MULTIPLE REGRESSION A Journal Article Advanced Psychological Statistics Psy520M Presented by: FLORABEL S. SUAREZ
    2. 2. <ul><li>Steven K. Hedden, University of Iowa </li></ul>Prediction of Music Achievement in the Elementary School Journal of Research in Music Education, Vol. 30, No.1 (Spring, 1982), pp. 61-68
    3. 3. The Research Problem The purpose of the present investigation was to evaluate a set of variables as predictors of music achievement in elementary general music classes. The research question was: Which of the five variables (i.e., academic achievement, attitude toward music, self-concept in music, music background, and gender) is the best single predictor of music achievement?
    4. 4. Description of Variables <ul><li>INDEPENDENT VARIABLES </li></ul><ul><li>DEPENDENT VARIABLE </li></ul>Academic achievement – Iowa Test of Basic Skills (ITBS) Attitude toward music – Attitude Toward Music Scale (ATMS) Self-concept in music – Self-Concept in Music (SCIM) Scale Music background – Music Background (MB) Scale Gender Music achievement – Music Achievement Test (MAT) , Level 1 predictor variables ( ‘x’ ) criterion variable (‘y’)
    5. 5. Appropriateness of the Analysis Number and type of variables: – use of 2 or more predictor/IV variables of an interval/ratio scale of measurement. Although another predictor, i.e. gender, of nominal level was added, multiple linear regression accommodated the data by means of coding – use of one dependent variable (i.e., music achievement) that is an interval/ratio level of measurement.
    6. 6. The Procedure T he study had chosen a group of fifth- and sixth-grade students from two schools who were attending regularly scheduled music classes that were taught by a music specialist who had been teaching in her present school for at least five years. The administrative procedures for testing were straightforward. The music specialist administered the ATMS, SCIM, and MB and the investigator administered the MAT one week later in the presence of the specialist. In an effort to control for possible order effects, one fifth-grade and one sixth-grade class at each school had a test order of ATMS, SCIM, and MB; the remaining class at each grade level had a test order of SCIM, ATMS, and MB. ITBS scores, on the other hand, were transcribed from school records.
    7. 7. Statistical Output
    8. 8. Statistical Output – Intercorrelations
    9. 9. Statistical Output – Intercorrelations
    10. 10. Statistical Output
    11. 11. Regression Analysis
    12. 12. Regression Analysis
    13. 13. Regression Analysis
    14. 14. Regression Analysis
    15. 15. Hypothesis: H 0 : R 2 = 0 H A : R 2 > 0 R 2 is estimated to be 33.64% (school one) R 2 is estimated to be 60.84% (school two) What are the null and alternative hypothesis for testing whether the % of total variance in music achievement that is explained together by ITBS, SCIM, MB, ATMS and Gender is significantly different from zero?
    16. 16. Hypothesis: What are the null and alternative hypothesis for testing whether the regression coefficient for music achievement is significantly different from zero?
    17. 17. Results of Analyses For school one: Academic Achievement: Attitude towards music: Self-concept in music: Music background: Gender:
    18. 18. Conclusion The major research question posed in the present investigation asked about the magnitude of the relationship between music achievement and a set of predictor variables-academic achievement, attitude toward music, self-concept in music, music background, and gender. Of the variables investigated in the present study, the academic achievement measure (ITBS) clearly was the best single predictor of music achievement. For school one, 25% of the variance in MAT scores could be attributed to academic achievement. The corresponding figure for school two was 40% .
    19. 19. Thank You !
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