How Reading at Home and Low SES Affect Literacy Development - Statistics Project Example

How Reading at Home and Low SES Affect Literacy Development How Reading at Home and Low SES Affect Literacy Development Contents How Reading at Home and Low SES Affect Literacy Development 1 Abstract 2 Research question: 3 Introduction 3 Literature review 4 Method 6 Research hypothesis 6 Participants in the research 7 Analysis of the research 7 Results and Discussion 9 Conclusion 10 Reference 11 Appendix 13 Abstract The main objectives of the research were to find out how reading at home and social economic level may influence literacy development in First Grade Students in United States (US). The research was a good tool for finding out effects of low SES and reading at home to overall literacy development of First Grade students. This research entailed, intensively, focusing on the role of environment and the child’s identity/behavior. Using the data that was collected by the National Centre for Education Statistics in the US, I analyzed using spss version 22 and the results shows that students reading from home and those from low social economic status were behind their reading levels, or were performing poorly in classes. Research question: 1. Does poverty, low SES status, age and availability of TV at home influence reading T-Score. 2. Do different approaches to learning, self-control, externalizing and internalizing problem behaviors affects reading T-Score. Introduction The basics of literacy begins even before a child joins a school, this skill should be nurtured at home at early age, through exposure to texts and guidance from elders (McBride-Chang, 2014). If this is successfully done at early stages, it lays a foundation for the formal schooling and reduces the kids’ stresss in academic (McBride-Chang, 2014). Also it initiates a positive attitude of the child, towards literacy. Most of the kids from low socioeconomic status lack an opportunity for material possession and are deprived of the basics of literacy (Sénéchal, 2012). Also, they may lack parental involvement in literal works. The literacy skills help all students not only to acquire languages and study literature but also to understand and pursue other courses. Students from low SES have a disadvantage of their surrounding or regions do not have facilities which can help in boosting their literacy level (Koh, 2012). Libraries, books store social reading places can act as a tool of raising literacy morale in kids. In most cases kids from rural are spatially removed from these literacy-rich centers. Liana (2009) shows how children from a poor SES are prone to poor brain development due to lack of exposure. There are a number of different and complex factors that may hinder well-being of kids and their achievement level at First Grade. Some of the factors are beyond school grasp. In this case, a good example would be the family wealth or their parent’s literacy level which has a strong positive correlation between the students achievement (Knight, 2009). The fact is that, most of the poor people do not have a deep and good understanding of education; thus, results to low children achievement in school. In the case of knowledge, literacy is a socially constructed ability, and not in seclusion for grade one students learn a lot through participation in school and not at home. Thus, first grades students that take their classes at home are always facing hard condition in learning, since it is involving. The environment in which they are learning in lack resources/materials and activities for a strong foundation of literacy and language. This first Grade kids’ face a hard time of learning as majorly literacy is well learned through participation (Lott &Bullock, 2007). Literature review Through the analysis of scholars’ work and articles, the researcher was able to come up with three themes. First theme is that Students from low SES are in a deficiency in resources necessary to pursue their literacy skills McBride-Chang, (2014). They find themselves in an environment without necessary tools that can help them in reading. These conditions make their progress difficult (Hair, 2010). The tools help students, prior to joining the formal education system, and make them have primary experience in communication. The lack of foundation makes them lags behind in academic performance for they lack preparedness of the formal literacy. Eventually, they may end up failing in their progress; any students’ future performance can be traced back to their early literacy experience in the home (Booth, 2013). In contrary, Kirby (2008) specifies that it doesn’t matter how many books or resources one has in his/her disposal what matters a lot is what the person is doing with them. The second theme is brought forward by Street, (2014) who strongly agrees that the literacy level of parents has a high impact to student’s performances in academics. The greater the level of literacy in the parents, the more the students will be nurtured in an environment with literacy atmosphere (Hogg, 2008). Parents or guidance with higher education qualification are more involved with their student’s development at school than those whose literacy level is very low for most of the time they may lack the knowledge of what is required to be done (Lipina & Colombo, 2009). They also find themselves aiding their kids in matters related to academics. In addition, this parent knows how to nurture their kid’s literacy attitude and understand how crucial it is for them to support their children. This rapport grows, and students develop a positive mentality towards reading Skwarchuk (2014). This situation makes poor SES parents have a significant problem, when it comes to nurturing their kid’s school life. Also, they lack material possession that can be used in developing literacy of their children. This makes students lack an opportunity to acquire some skills like phonological processing and adopting reading culture (Wang, 2013). Furthermore, they lack reading knowledge, which is an elementary literacy skill. Street, (2014) furthers Skwarchuk et al. (2014) finding, as he concurs that parents play a significant role in early child’s literacy skills. Knight et al. (2009) also shows how students from low SES are affected since they tend to join schools (even kindergarten) when they are already grown up. According to them, this latency creates a late introductory in literacy, which significantly affects the kids literacy development. The late aged students struggle to catch up with their wits, affluent peers, and it turns out to be out of their reach. This problem drags them all through their learning process and acts as the hindrances of them archiving in academics (Koh et al., 2012). The third theme that arose is the conviction or the beliefs on education among the adults from low SES. Most of them dogmatically think that education belongs to the classy and rich people, not to them. This perception makes them deny their children the basic need of literacy. This dogma is passed on to the students thus making them put less effort in pursuing of literacy; eventually they lag behind in performance (Wang, 2013). Most importantly, maternal influence is high since they spend lots of time with their kids (Sénéchal, 2012). Therefore, mothers with higher literacy level tend to foster their young ones about literacy. It can, therefore, be deducing that students that conduct their study at home are disadvantaged for there is less strict rule to be followed. Also they lack participation, which is the best way of learning. Method Research hypothesis The null hypothesis that was set at the start of the study, that was to be tested, was that; There exist a positive relationship between reading T-Scores in Grade One students and poverty, low SES status, age and availability of TV at home. There exist a significant difference in reading T-score in Grade one students that have different approaches to learning, self-control, externalizing and internalizing problem behaviors in United States. I set my level of significance at 0.05. The low level of significance was to minimize a type I error of rejecting the null hypothesis when it is correct (Pituch, 2013). Participants in the research The dataset collected by the National Centre for Education Statistics in the US consisted students that are native speakers of English from variaous backgrounds. The students (more than 500) were my scope of the study. I subdivided them into categories that were based on student’s socioeconomic level, in order to increase the precision of my research. From the subgroup, I used simple randomization process in order to come up with a smaller sample. Gupta, (2000) shows how important it is to use randomization when selecting a sample in order to reduce chances on confounding variables. Confounding variables are as the results of a badly designed experiment; they tend to distort the relationship between the controlled variable and the response. This method does not necessarily guarantee that there will be no association. Instead, it ensures that lurking variables are not likely to have an impact on the conclusion (Booth, 2013). Analysis of the research On the collected data, I performed some qualitative analysis such as Analysis of Variance. I used regression analysis to determine the degree of the relationship between the response variable (dependent) and explanatory variables (independent variable). The response variable was reading T-Score and independent variables: different approaches to learning, self-control, externalizing, internalizing problem behaviors, poverty, low SES status, age and availability of TV at home. Most of the data collected was analyzed using IBM SPSS Statistics software version 22. I applied ANOVA analysis in order to test the variability of students reading T-Scores with independent variables. Variances in the two groups were compared, in order to find any significant difference between them. Regression analysis was performed on the same sample data, and the results obtained were equal to those from the ANOVA test. (Gelman, 2014) stipulates that regression models will yield the same results of ANOVA for both analyzed variability of the data. With inferential statistics, I was able to conclude beyond immediate data for it helped me in understanding the nature of the relationship between the two groups (students reading at home and those reading on school bases). Mandel, (2012) shows how this is giving the difference between the reading T-score and some variables. SES status, poverty level, age, interpersonal, approaches to reading and self-control had a positive correlation and thus I did not reject the null hypothesis. In addition number of siblings, externalizing and internalizing problem behavior had a negative correlation thus I rejected their hypothesis that they had an effect on reading T-Score. In the two-tailed analysis of Pearson correlation (0.004 level of significance), the analysis showed that there was a moderate positive correlation (0.411). In accordance with Pituch et al. (2013), this depicts that the direction of relationships was deviating, and thus there was a significance difference in reading T-score and different independent variables under study. By the same token, I analyzed the performances of students, on basis of SES, in which the same results were obtained; there existed a significant difference in reading T-Score in kids from low SES (Wilcox, 2012). Results and Discussion The different techniques of statistical data analysis show that, indeed, there existed a negative relationship in reading T-Score of students depending on SES, different reading habits, age approaches to reading, interpersonal and self-control in first Grade students of the United States. This was due to some of the facts, such as the lack of adequate resources in supporting the academic progress of the student. Students from low SES mostly are faced by this problem, making their performance below average as compared to those on the school basic program. In addition, they may be performing poorly due to lack of participation for (Sénéchal, 2012) states that a child learns a lot through his/ her participation that when taught. This may make the student have inadequate intelligence, which is required for the startup of literacy life. Kids from low SES are mostly from families with low literacy level. Thus the guardians or parent lack the necessary skill to support them in literacy. This shortage may risk having the kids to miss parental support in academic progress. Lastly, the dogmatic thought of education belongs to the rich may have a big impact on the literacy performance of students. This perception creates a negative attitude to literacy in students from low SES, and they do not put their efforts on academic issues (McBride-Chang, 2014). The results of this are continuous lagging in academics as their peers shine. To reduce this McBride-Chang, (2014) emphases that teachers at school may be used to give motivational speeches of how crucial literacy can be of help in people’s life. Conclusion In this research, I found out that, mostly, the factors affecting the Grade one students, originate from the early childhood. Few obstacles can be remedied for literacy improvement in low SES students, such as in boosting student’s morale, teaching different techniques in approaching learning and child self-control by guidance and counseling. The results show how the basics requirement of an education system can be provided to the government, in order to boost literacy level. In addition, the government can conduct some campaigns for improving or elevating the level of literacy level among the low SES. Through the campaigns, the dogmatic mind/ belief will be eradicated, and the low SES students will value education as well as perform like students from high SES. This research was appropriate because the findings were viable. In the future, given a chance to conduct any research on the same topic, I will increase the scope of the geographical basis so as to find out the adversely affected regions in the US. Reference Booth, A., & Dunn, J. F. (Eds.). (2013). Family-school links: How do they affect educational outcomes? Routledge. Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2014). Bayesian data analysis (Vol. 2). Chapman & Hall/CRC. Gupta, S., & Kapoor, V. (2000). Fundamentals of mathematical statistics: A modern approach (10th rev. Ed.). New Delhi: Sultan Chand. Hair, J. F. (2010). Multivariate data analysis. Hogg, R., & Craig, A. (1978). Introduction to mathematical statistics (4th Ed.). New York: Macmillan. https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2013029 Knight, G. P., Roosa, M. W., & Umaña-Taylor, A. J. (2009). Studying ethnic minority and economically disadvantaged populations: Methodological challenges and best practices. American Psychological Association. Koh, H. K., Berwick, D. M., Clancy, C. M., Baur, C., Brach, C., Harris, L. M., & Zerhusen, E. G. (2012). New federal policy initiatives to boost health literacy can help the nation move beyond the cycle of costly ‘crisis care’. Health Affairs, 10-1377. Lipina, S. J., & Colombo, J. A. (2009). Poverty and Brain development during childhood: An approach from cognitive psychology and neuroscience. American Psychological Association. Lott, B., & Bullock, H. E. (2007). Psychology and economic injustice: Personal, professional, and political intersections. American psychological association. Mandel, J. (2012). The statistical analysis of experimental data. Courier Corporation. McBride-Chang, C. (2014). Childrens literacy development. Routledge. Pituch, K. A., Whittaker, T. A., & Stevens, J. P. (2013). Intermediate Statistics: A modern approach. Routledge. Sénéchal, M. (2012). Child language and literacy development at home. Handbook of family literacy, 38-50. Skwarchuk, S. L., Sowinski, C., & LeFevre, J. A. (2014). Formal and informal home learning activities in relation to children’s early numeracy and literacy skills: The development of a home numeracy model. Journal of experimental child psychology, 121, 63-84. Street, B. V. (2014). Social literacies: Critical approaches to literacy in development, ethnography and education. Routledge. Wang, X. (2013). The Influences of Parental Expectations on Childrens Academic Achievements: A Comparative Analysis of the United States and China. Wilcox, R. R. (2012). Introduction to robust estimation and hypothesis testing. Academic Press. Appendix Table 1 shows the data as analyzed in SPSS child id school id READING TSCORE GENDER AGE NUMBER OF SIBLINGS POVERTY SES READ BOOK TV HOME LEARN Column1 SELF-CONTROL INTERPERSONAL EXTERNALIZING OF PROBLEM BEHAVIOUR INERNALIZINF OF PRBLEM BEHAVIOUS FILTERS 0842013C 842 22.283 1 77.13 5 1 1 2 1 2 1.5 1.75 3.2 3.25 1 0842013C 842 22.283 1 77.13 5 1 1 2 1 2 1.5 1.75 3.2 3.25 1 0089022C 89 43.326 2 79.23 1 2 4 3 1 2.33 3 2.8 1.4 1.25 1 0304001C 304 47.725 2 79.77 1 2 5 3 1 4 4 4 1 1.25 1 1181014C 1181 48.759 2 80.03 3 2 4 3 1 3 3 3.25 1.6 1.5 1 1181014C 1181 48.759 2 80.03 3 2 4 3 1 3 3 3.25 1.6 1.5 1 0300006C 300 51.465 1 81.07 1 2 4 4 1 2.83 3.25 2.8 1.5 2 1 3016001C 3016 40.739 2 81.43 2 2 3 2 1 2.83 3.75 4 1.2 1.75 1 0717006C 717 65.552 2 83.03 1 2 3 3 1 2.67 2 2.4 1.8 1.75 1 0512003C 512 46.047 1 83.3 2 2 4 4 1 2.67 3 3.2 2.2 1 1 0824021C 824 49.158 2 83.53 0 2 2 4 1 1.83 2 2 2.8 1.25 1 0754002C 754 35.838 2 84.17 2 2 2 4 1 2 1.75 1.75 1 1.75 1 3022022C 3022 55.616 1 84.23 1 2 3 3 1 2.5 3.25 3.4 1.4 2.75 1 1209017C 1209 60.776 1 84.3 2 2 5 4 1 3.17 2.75 3 2.2 2 1 0437006C 437 44.992 1 84.5 3 1 2 1 1 2 2.5 1.8 3.4 3.25 1 0407009C 407 37.769 2 84.6 3 2 5 4 1 2.5 3.25 3 1 1.5 1 0078009C 78 64.918 1 84.7 1 2 5 3 1 3.83 3.33 3.8 1.75 1.5 1 0555023C 555 68.006 2 85.27 0 2 4 3 1 3.5 3.25 3 2 2 1 0376013C 376 56.892 1 85.37 5 2 4 3 1 4 4 4 1 1 1 1119009C 1119 47.575 1 85.7 1 2 5 4 1 3 3.25 2.8 2 1.5 1 0849003C 849 47.105 1 86.7 2 1 1 2 1 2.67 2 2.6 3.2 1.5 1 0911011C 6116 59.858 2 86.8 1 2 5 3 1 4 3.67 3.8 1.2 1.5 1 0845015C 845 26.428 1 87 4 1 1 4 1 3 2.5 2.6 3.2 1.75 1 0717021C 717 47.312 1 87.1 1 2 5 4 1 1.83 2.5 2.8 2.4 2 1 0010012C 10 55.146 2 87.5 1 2 4 2 1 3.5 4 3.8 1 1.75 1 0717015C 717 44.941 2 87.5 1 2 5 3 1 3.5 3.75 3.8 1 1.75 1 1061011C 1061 61.893 1 87.67 1 2 4 3 1 3.83 3.5 3.8 1 1.25 1 0840010C 840 48.1 1 87.77 1 2 5 4 1 2.17 2.25 2 2.4 2.25 1 0880004C 5467 51.05 2 89.37 2 1 1 4 1 3.5 3.5 3.6 1.6 1 1 3027005C 3027 61.308 2 89.53 2 2 4 4 1 3.83 3.25 3 1.6 1 1 0750008C 750 48.166 1 89.77 3 1 1 2 1 2.33 2.25 2.2 1.6 1.75 1 0731003C 731 62.06 2 90.7 1 1 2 4 1 3.83 4 4 1 1.25 1 0240013C 240 43.969 2 90.53 3 1 2 4 1 2.83 2.5 2.4 2.4 2.5 1 0434004C 434 54.345 1 91.1 1 2 3 3 1 2.17 3 2.6 2 1.75 1 0507020C 507 60.914 1 91.3 1 2 5 3 1 4 3.5 3.8 1 1.5 1 0818021C 818 56.044 2 91.9 1 2 2 1 1 3.83 3.25 3.6 1.8 1.25 1 0811011C 5688 56.491 1 92.5 3 1 1 4 1 3.67 3 2.8 2.6 1.5 1 0466004C 466 56.845 2 92.63 2 2 2 4 1 3.67 4 3.8 1 1 1 0770025C 4120 40.055 1 94.47 3 1 1 4 1 2.83 3.25 2.8 1.8 1.75 1 Table 2 shows the results obtained from ANOVA test ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 3255.010 11 295.910 5.726 .000b Residual 1447.008 28 51.679 Total 4702.018 39 a. Dependent Variable: READING T-SCORE b. Predictors: (Constant), INTERNALIZING PROBLEM BEHAVIOURS, SES MEASURE, GENDER, HOW OFTEN YOU READ TO CHILD, AGE (MONTHS), NUMBER OF SIBLINGS, SELF-CONTROL, EXTERNALIZING PROBLEM BEHAVIORS, APPROACHES TO LEARN, POVERTY LEVEL, INTERPERSONAL Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -1.755 42.576 -.041 .967 GENDER -2.694 2.903 -.124 -.928 .361 AGE (MONTHS) .463 .358 .196 1.294 .206 NUMBER OF SIBLINGS -3.880 1.238 -.455 -3.135 .004 POVERTY LEVEL 5.432 5.484 .224 .990 .330 SES MEASURE -.114 1.534 -.016 -.074 .941 HOW OFTEN YOU READ TO CHILD -.747 1.608 -.060 -.464 .646 APPROACHES TO LEARN 9.460 3.265 .608 2.898 .007 SELF-CONTROL -1.769 4.452 -.115 -.397 .694 INTERPERSONAL -1.792 5.138 -.117 -.349 .730 EXTERNALIZING PROBLEM BEHAVIORS -.443 2.709 -.030 -.164 .871 INTERNALIZING PROBLEM BEHAVIOURS -.055 3.125 -.003 -.018 .986 a. Dependent Variable: READING T-SCORE Table 3 shows the results of correlation tests Paired Samples Correlations N Correlation Sig. Pair 1 READING T-SCORE & GENDER 40 .123 .451 Pair 2 READING T-SCORE & AGE (MONTHS) 40 .421 .007 Pair 3 READING T-SCORE & NUMBER OF SIBLINGS 40 -.625 .000 Pair 4 READING T-SCORE & POVERTY LEVEL 40 .440 .004 Pair 5 READING T-SCORE & SES MEASURE 40 .411 .008 Pair 6 READING T-SCORE & HOW OFTEN YOU READ TO CHILD 40 .117 .470 Pair 7 READING T-SCORE & HAVE TV AT HOME 40 . . Pair 8 READING T-SCORE & APPROACHES TO LEARN 40 .582 .000 Pair 9 READING T-SCORE & SELF-CONTROL 40 .480 .002 Pair 10 GENDER & INTERPERSONAL 40 .302 .058 Pair 11 GENDER & EXTERNALIZING PROBLEM BEHAVIORS 40 -.430 .006 Pair 12 GENDER & INTERNALIZING PROBLEM BEHAVIOURS 40 -.333 .036 Paired Samples Statistics Mean N Std. Deviation Std. Error Mean Pair 1 READING T-SCORE 50.01455 40 10.980189 1.736120 GENDER 1.48 40 .506 .080 Pair 2 READING T-SCORE 50.01455 40 10.980189 1.736120 AGE (MONTHS) 86.1590 40 4.63328 .73259 Pair 3 READING T-SCORE 50.01455 40 10.980189 1.736120 NUMBER OF SIBLINGS 1.93 40 1.289 .204 Pair 4 READING T-SCORE 50.01455 40 10.980189 1.736120 POVERTY LEVEL 1.73 40 .452 .071 Pair 5 READING T-SCORE 50.01455 40 10.980189 1.736120 SES MEASURE 3.20 40 1.506 .238 Pair 6 READING T-SCORE 50.01455 40 10.980189 1.736120 HOW OFTEN YOU READ TO CHILD 3.20 40 .883 .140 Pair 7 READING T-SCORE 50.01455 40 10.980189 1.736120 HAVE TV AT HOME 1.00 40 .000 .000 Pair 8 READING T-SCORE 50.01455 40 10.980189 1.736120 APPROACHES TO LEARN 2.9830 40 .70573 .11159 Pair 9 READING T-SCORE 50.01455 40 10.980189 1.736120 SELF-CONTROL 2.9813 40 .71129 .11246 Pair 10 GENDER 1.48 40 .506 .080 INTERPERSONAL 2.9987 40 .71777 .11349 Pair 11 GENDER 1.48 40 .506 .080 EXTERNALIZING PROBLEM BEHAVIORS 1.8063 40 .74509 .11781 Pair 12 GENDER 1.48 40 .506 .080 INTERNALIZING PROBLEM BEHAVIOURS 1.7063 40 .59616 .09426 Table 5shows results from the correlation tests Correlations Control Variables GENDER AGE (MONTHS) NUMBER OF SIBLINGS POVERTY LEVEL SES MEASURE HOW OFTEN YOU READ TO CHILD HAVE TV AT HOME APPROACHES TO LEARN SELF-CONTROL INTERPERSONAL EXTERNALIZING PROBLEM BEHAVIORS INTERNALIZING PROBLEM BEHAVIOURS READING T-SCORE GENDER Correlation 1.000 -.173 -.235 .219 .026 -.003 . .221 .282 .280 -.423 -.315 Significance (2-tailed) . .294 .150 .180 .874 .986 . .177 .081 .085 .007 .051 df 0 37 37 37 37 37 37 37 37 37 37 37 AGE (MONTHS) Correlation -.173 1.000 .051 -.409 -.392 .241 . .104 .050 -.061 -.007 -.195 Significance (2-tailed) .294 . .758 .010 .013 .139 . .529 .761 .711 .965 .234 df 37 0 37 37 37 37 37 37 37 37 37 37 NUMBER OF SIBLINGS Correlation -.235 .051 1.000 -.412 -.297 -.120 . .305 .007 .042 .118 .073 Significance (2-tailed) .150 .758 . .009 .067 .465 . .059 .968 .798 .473 .660 df 37 37 0 37 37 37 37 37 37 37 37 37 POVERTY LEVEL Correlation .219 -.409 -.412 1.000 .754 .100 . -.114 .166 .194 -.449 -.215 Significance (2-tailed) .180 .010 .009 . .000 .543 . .488 .311 .238 .004 .188 df 37 37 37 0 37 37 37 37 37 37 37 37 SES MEASURE Correlation .026 -.392 -.297 .754 1.000 .168 . .046 .267 .262 -.354 -.057 Significance (2-tailed) .874 .013 .067 .000 . .306 . .783 .100 .107 .027 .732 df 37 37 37 37 0 37 37 37 37 37 37 37 HOW OFTEN YOU READ TO CHILD Correlation -.003 .241 -.120 .100 .168 1.000 . -.035 .024 -.075 -.132 -.362 Significance (2-tailed) .986 .139 .465 .543 .306 . . .833 .883 .652 .424 .023 df 37 37 37 37 37 0 37 37 37 37 37 37 HAVE TV AT HOME Correlation . . . . . . 1.000 . . . . . Significance (2-tailed) . . . . . . . . . . . . df 37 37 37 37 37 37 0 37 37 37 37 37 APPROACHES TO LEARN Correlation .221 .104 .305 -.114 .046 -.035 . 1.000 .692 .752 -.362 -.387 Significance (2-tailed) .177 .529 .059 .488 .783 .833 . . .000 .000 .023 .015 df 37 37 37 37 37 37 37 0 37 37 37 37 SELF-CONTROL Correlation .282 .050 .007 .166 .267 .024 . .692 1.000 .891 -.611 -.384 Significance (2-tailed) .081 .761 .968 .311 .100 .883 . .000 . .000 .000 .016 df 37 37 37 37 37 37 37 37 0 37 37 37 INTERPERSONAL Correlation .280 -.061 .042 .194 .262 -.075 . .752 .891 1.000 -.594 -.435 Significance (2-tailed) .085 .711 .798 .238 .107 .652 . .000 .000 . .000 .006 df 37 37 37 37 37 37 37 37 37 0 37 37 EXTERNALIZING PROBLEM BEHAVIORS Correlation -.423 -.007 .118 -.449 -.354 -.132 . -.362 -.611 -.594 1.000 .438 Significance (2-tailed) .007 .965 .473 .004 .027 .424 . .023 .000 .000 . .005 df 37 37 37 37 37 37 37 37 37 37 0 37 INTERNALIZING PROBLEM BEHAVIOURS Correlation -.315 -.195 .073 -.215 -.057 -.362 . -.387 -.384 -.435 .438 1.000 Significance (2-tailed) .051 .234 .660 .188 .732 .023 . .015 .016 .006 .005 . df 37 37 37 37 37 37 37 37 37 37 37 0 Read More