Which Came First: The Reading or The Math Relationship Between Reading Skills and Mathematics Achievement

1. Which Came First: The Reading or The Math? Relationship Between Reading Skills and Mathematics Achievement  Gloria T. Bilaye-Benibo, Ed.D Corpus Christi Independent School District

2. INTRODUCTION Professional Background 20+ years in public and private education Low-performing high school campuses Counselor for at-risk populations Drop-out rate Math failure � best predictor of drop-out 9th grade � crucial graduation predictor Honest discussions with students

3. The leading area of failure in school for most students in the U.S. and in Texas, is mathematics (Geary and Hamson, 2000, Bracey, 1996). In 2006, only 49% of 9th graders passed math compared to 87% in reading in ESC Region 2 (TEA, 2007). Studies have addressed performance in mathematics and possible associations with gender, ethnicity, and/or socio-economic status (Signer, 2006). Other studies have associated reading skills to mathematics performance in elementary and middle schools (Grimm, 2006; Reikeras, 2006).

4. To examine the relationship between a high school student�s proficiency in specific reading skill areas and his/her performance in mathematics. To determine the extent to which the relationship was mitigated by the gender, ethnicity and socioeconomic status of the students.

5. Poor performance in mathematics among high school students remains a concern to all stake holders in education (Woodward & Brown, 2006). Reading skills of students have been suggested as a notable reason for the poor performance in mathematics (Reikeras, 2006). Previous studies have related scores in general reading skills to students� overall mathematics performance (Grimm, 2006; Spielhagen, 2006). Studies yet to focus primarily on the possible association between specific reading skills (such as those measured by TAKS objectives) and the mathematics achievement among high school students in South Texas.

6. Transfer of Learning Theory Takes place in education whenever existing knowledge, abilities and skills enhance or undermine the performance of new tasks (Perkins & Salomon, 1992). Involves the application of skills, knowledge and even attitudes from one learning situation to another (Clark, 2000). Serves as a back drop for discussions on the relationship between specific skills in one area (reading) and the achievement in another (mathematics).

10. What are the relationships between Basic Understanding reading skill and achievement on the individual mathematics objectives among ninth grade students, as measured by the Texas Academic Knowledge and Skills?

11. What are the relationships between Literacy Technique reading skill and achievement on individual mathematics objectives among ninth grade students, as measured by the Texas Academic Knowledge and Skills?

12. What are the relationships between Critical Analysis reading skill and achievement on individual mathematics objectives among ninth grade students, as measured by the Texas Academic Knowledge and Skills?

13. What are the relationships between the three reading skills (Basic Understanding, Literacy Technique and Critical Analysis) and achievement on all ten mathematics objectives among ninth grade students, as measured by the Texas Academic Knowledge and Skills?

14. Are there gender, ethnicity and socio-economic status differences among bivariate relationships between Basic Understanding reading skill, Literacy Technique reading skill, and Critical Analysis reading skill on one hand and the individual mathematics objectives on the other hand?

16. Ex post facto, correlational study. Utilized tests that have already taken place and as a result the independent variables could not be manipulated and no causal inferences was made (Field, 2005).

17. Consisted of Reading and Mathematics scores from the spring 2007 Texas Academic Knowledge and Skills (TAKS) for the 9th grade. The non-probability sample consisted of 2,353 9th graders who had taken the 2007 TAKS test in spring of 2007 and had test scores for both the reading and mathematics portions of the test. Gender, socioeconomic status and ethnicity were the only demographic variables used to describe the study participants.

18. Comprised of 3 items. TAKS Reading Objectives. Described specific reading skills of 9th grade students, as measured by their scores in the spring 2007 TAKS test.

20. Comprised of 10 items. TAKS mathematics objectives. Scores obtained by same 9th grade students as measured by their performance in the 2007 TAKS test.

22. Limited to 9th grade high school students who participated in the Spring 2007 TAKS test. Limited to only five non-specialty high schools in a large school district in South Texas. Measurements and skills were based solely on the TAKS scores obtained by the participants. All generalizations were limited to the population studied.

23. The student population in the five high school campuses were assumed to be similar in demographic composition. The TAKS scores used were obtained under testing conditions consistent with proper testing environment. Each of the students was assumed to have only two sets of TAKS scores attained in the same year of testing � one for reading and one for mathematics.

24. The study promised three contributions to the field of inquiry: Policy Formulation: The findings of this research could assist in clearer policy formulations among district administrators, state education agencies and even university faculty. Methodology Redirection: In another way, this study may contribute to methodological development in educational research. Theoretical Validation: Lastly, this study would contribute to or modify existing learning theories in education.

26. Two types of correlational analyses were performed. Each of the reading skills and mathematics achievement areas/objectives.

27. A series of Pearson Product Moment Correlation Coefficient (Pearson�s r) was performed to answer the first three research questions in order to determine the relationship between the two variables: Scores on each of the 3 TAKS reading objectives (X) Scores on each of the 10 TAKS math objectives (Y). Describes the degree of association between two variables as well as the direction (positive or negative) of the relationship (Meltzoff, 2004). Coefficient of determination, r2, was used to examine the practical significance of each of the bivariate associations (Meltzoff, 2004).

28. Performed to answer the fourth research question. Determine the nature and extent of the relationship between the two sets of variables. Shows how much the variance in one set of variables provides an explanation for the other set of variables (French et al, 2005;Weenink, 2003). Intent is to maximize correlations between variables represented by two set of data (Stevens, 2002).

29. Canonical variates, consisting of the dependent (mathematics objective scores) and independent (reading skills) variables, were formed. Canonical variates were correlated in more than one way and yielded three canonical variable pairs numbered 1, 2 and 3. Each pair of canonical variates yielded a separate canonical correlation coefficient (Polit, 1996). Each canonical correlation coefficient was evaluated on the basis of its significance.

30. Standardized canonical weights associated with the ten math objectives scores and the three reading skills are were computed. Weights describe the magnitude of importance of the individual dependant and independent variables in forming the canonical variates pairs (Polit, 1996). Structure coefficient, also known as �Loading�, associated with the dependent variables and the independent variables were computed. Loadings describe the correlations between the two sets of variables and the canonical variates and are useful in describing the actual meanings of the canonical correlations (Polit, 1996).

31. Multivariate test of significance represented by Wilkes� Lambda (?). Defined as: ? = (1 � R�c1) (1 � R�c2) (1 � R�c3) Rc X proportion of variance=redundancy coefficient Redundancy coefficient describes how redundant one set of variable is, given the other set . Practical significance of the correlation

32. The fifth research question was answered by computing the bivariate association for each group (based on gender, ethnicity and socio-economic status) Fisher�s r-to-z transformation was employed to estimate the differences between each pair of correlation. Fisher�s r-to-z transformation converts Pearson r to the normally distributed variable z (Fields, 2005).

33. Each r was transformed into a z-score, using a table (Kirk, 1999). The formula used to compute the z-score: z = Z1 - Z2 v 1/ (n � 3) The obtained z-scores were tested against the critical z-score of 2.58 to examine the difference between each pairs of simple correlation at the .01 level of significance.

34. For gender, the male sample was treated as the reference group Sample size for females, n = 1184, was used in calculation of the z-score. For socioeconomic status, the �all others� sample was treated as the reference group Sample size for the economically disadvantaged, n = 1227, was used in calculation of the z-score. Ethnicity was recoded to reflect Hispanics and Non-Hispanics. The non-Hispanics sample was treated as the reference group. Sample size for the Hispanic, n = 663, was used in the calculation of the z-score.

35. RESULTS:

36. PROFILE OF RESEARCH SUBJECTS

37. SUMMARY OF MEASURES

38. SUMMARY OF MEASURES

40. CANONICAL ANALYSIS: SUMMARY

41. Basic Understanding reading skill and achievement on the individual mathematics objectives were all statistically significant in their correlation at the .001 level and explained variations ranged from .11 (11%) to .19 (19%).

42. Literacy Techniques reading skill and achievement on individual mathematics objectives were all statistically significant in their correlation at the .001 level and explained variations ranged from .10 (10%) to .18 (18%).

43. Critical Analysis reading skill and achievement on individual mathematics objectives were all statistically significant in their correlation at the .001 level and explained variations ranged from .09 (9%) to .19 (19%).

44. Wilkes� Lambda (?) of .65 was statistically significant, F (30, 7016) = 35.96, p < .01. The canonical correlation coefficient was .58 (coefficient of determination = 33.64%) Accounted for 97.49% of the explained variance. Weights (Importance): Linear Equations / Tool-Strategies(.28). Basic Understanding(.43). Loading (Meaningful): All variables > .3 All meaningful.

45. Dependent variables (Mathematics) The percentages of the variance was 52.99% The redundancy coefficient (of I.V.) was .18. Independent variables (Reading) The percentages of the variance was 73.85% The redundancy coefficient (of D.V.) was .25.

46. Gender: Correlation coefficient between Critical Analysis and Function Properties was .37(males) and .30 (females) Favoring the males Socioeconomic Status: Correlation coefficient between Critical Analysis and Linear Functions (z = 2.79, p < .01), Dimensional Representations (z = 3.15, p < .01), Measurement Concepts (z = 3.15, p < .01), Proportional Relationship (z=2.79, p < .01) Favoring the �all others.� Ethnicity: Correlation coefficient between Literacy Techniques and Functional Relationship (z = 2.83, p < .01) and Linear Equations (z = 2.83, p < .01) Favoring the Non-Hispanics.

47. CONCLUSION:

48. The statistically significant relationships between each of the three reading skills and the ten TAKS mathematics objectives suggest that reading skills were positively associated with mathematics achievement. The findings of the study support the transfer of learning theory- the ability to make connections between totally different situations and groupings.

49. Basic Understanding, Literacy Techniques and Critical Analysis reading skills are important factors in 9th grade` students mathematics achievement in the school district studied. Basic Understanding had the most �weight� in terms of importance in the relationship to a student�s performance in all the mathematics objectives scores. Linear Equations and Tool-Strategies had the most �weight� and were the most important among the dependent variables.

50. On the basis of the redundancy coefficient, the mathematics objectives scores better explained the reading skills than the reading skills explained the mathematics objective scores. The canonical correlation analysis suggests that the mathematics skills enhanced the reading scores.

51. This implies that the students who are proficient in reading were already excelling in mathematics in the first place. It may be also mean that the reading is enhanced by analytical skills inherent in the ability to solve mathematics problems. The statistical significance could be due to at least two factors: Large sample size (n=2,353) and Relative homogeneity (71.80%) of the sample.

52. On the basis of gender, there were no differences between the females and the males in terms of the relationship between Basic Understanding reading skill and each of the Mathematics Objectives. There were some differences between the Literacy Techniques and Critical Analysis and some of the mathematics objectives scores favoring the males.

53. Socioeconomic status was a mitigating factor in terms of the relationship between Literacy Techniques reading skill and each of the Mathematics Objectives. Some differences exit between the Basic Understanding and Critical Analysis reading skills and almost half of the mathematics objective scores all favoring �others�. Economically disadvantaged students were less favored in the association between reading skills and mathematics achievement.

54. Ethnicity was not a mitigating factor in terms of the relationship between Basic Understanding as well as Critical Analysis reading skill and each of the mathematics objectives scores. Non-Hispanics show a difference in the relationship between Literacy Techniques reading skills and some of the mathematics objectives scores favoring the Non-Hispanics.

55. RECOMMENDATIONS

56. 9th grade reading and mathematics teachers may enhance students� achievement in mathematics team-teaching or co-teaching. TEA assessment development division should consider regrouping the TEKS so that the 10 specific mathematics TAKS objectives can be categorized into 3 major groups of objectives to mirror the TAKS reading. Teachers should pay particular attention when teaching Hispanic students, to ensure that they master the Literacy Technique reading skill as well as Linear Equations and Functional Relationship mathematics objectives.

57. Special attention should still be paid to the middle school females especially in their mastery of the Literacy Technique and Critical Analysis reading skills in an effort to reduce the gender differences by their 9th grade. District and campus administration and teachers to emphasize the mastery of the Basic Understanding reading skill as it carried the most weight in terms of importance. In preparing high school math teachers, colleges should emphasize the importance of tools- strategies in mathematics

58. A statewide or national data may show a more widely generalizable conclusion when compared to the data from five high schools in same school district. A more racially/ethnically representative sample that includes all existing categories listed by the census bureau should be used for a more accurate demographic generalization. A correlational study involving mathematics and other TAKS content areas should be done to determine the relationship, if any, between Mathematics and Science, Socials studies and English Language arts.

59. Replications of the research in form of a longitudinal study using students who participated in the 9th grade TAKS testing in years other than 2007 in order to establish consistency. Other standardized tests from other states could be used to determine if the correlations were due to a specific test instrument( like TAKS) or if they were knowledge-based. To better test the transfer of learning theory, an experimental design may be necessary to establish the occurrence of a time order in the relationship between reading skills and mathematics achievement.

60.  Thank You!