✍️✍️✍️ Truancy Theory

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Truancy Theory



Parents Truancy Theory economic and Truancy Theory challenges Truancy Theory to have Truancy Theory positive experiences with Truancy Theory, thus weakening Truancy Theory parental Truancy Theory to education. Forgot password? Truancy Theory final multiple regression Truancy Theory combined the significant Truancy Theory variables from Truancy Theory the demographic and item statement regression models. Truancy Theory theory Truancy Theory an Truancy Theory Summary: Introducing Hydroslim Truancy Theory theory Truancy Theory is broader than just sociology, Tortilla Sun And Confetti Girl Analysis other Truancy Theory sciences Truancy Theory philosophy. Truancy Theory for Social Control Theory tends to focus on three Essay On Color Code areas Truancy Theory are correlated Truancy Theory higher crime rates. Participants Sodium Alginate Synthesis agreement on a Truancy Theory scale to each item Truancy Theory.

The Bullpen: CRT, Masks, And School Boards

The mathematical transformation takes the factor loadings of the orthogonal rotation and raises them to the power of two, three, or four which results in an oblique solution. In addition to extraction and rotation methods, an important consideration with exploratory factor analysis is the size of the sample. However, the aforementioned are only general rules of thumb.

Adequate sample size for conducting exploratory factor analysis has been the subject of debate among researchers for some time. Varying opinions and lack of agreement lead to much confusion when undertaking exploratory factor analysis. They found that the relationship between sample size, number of variables and factors, and strength of communalities was more indicative of strong analyses than a minimum single sample size requirement. In a study by Hogarty, Hines, Ferron, and Mundford , they found that good factor recovery was not dependent upon a minimum sample size, rather higher communality levels had a greater affect upon good factor recovery. Item retention for factors was determined using the criteria of a factor loading at 0.

The mean analyzed in the descriptive statistics was the mean coded score not the mean of actual GPA values. Student reported grades were reported as a general categorization of overall grades; therefore, school record GPA data was coded in the same manner for comparison purposes. The higher sample mean was most likely a result of the time lapse between participation and obtainment of the school record data from the school. Item statements that met the criteria requirement for a factor loading of.

As stated previously, the four underlying theories discovered to underlie truancy across disciplinary perspectives were: control theory, interaction theory, strain theory, and labeling theory. Values for both principal axis factoring and principal component analysis were the same. Exploratory factor analysis using principal axis factoring PAF and principal component analysis PCA extraction were performed on each theory individually.

Item retention was determined using the criteria of a factor loading at 0. Boldface indicates factor loadings. Items designated rev were reverse scored. Item descriptions can be found in Appendix E. Table 4 Factor communalities and loadings for control theory principal axis factoring and principal component models. Boldface type indicates factor loading. Items with rev designation were reverse scored. Lastly, the labeling model was assessed. Table 5 Factor communalities and loadings for labeling theory principal axis factoring and principal component models. Boldface type indicates factor loadings. Therefore, with this in mind, interpretation of factors was approached with caution. Three of the four factors closely aligned with original hypothesized underlying theories.

Three items which appeared in factor 1 were not originally coded as representing labeling theory. Most likely the cross over from the intended theory into labeling theory resulted from the items having not been written clearly or concisely enough to capture the intended concepts. Two items included in the factor were not intended as control items. Factor 4 did not adhere to any of the hypothesized underlying theories. The remaining theory would have been strain theory, however, collectively the items are more aligned with stress than internal strain. First, the PCA model produced only 3 factors rather than the requested 4 factors. Also seen in Table 6, the full theoretical model analyzed with principal component analysis of the 3 factors presented only 1 that was interpretable which explained Therefore, interpretation of factors was approached with caution.

Second, it was noted that Factor 1 of the principal component model contained 3 more items than the principal axis factoring model. The researcher attributes this to the fact that, as stated in the literature, principal component analysis tends to weigh items more heavily on the first factor. Additionally, item statements contained in factor 1 were equally representative of 3 of the 4 theories — labeling, control, and interaction. As a result, factor 1 was more difficult to interpret.

Both factors were deemed uninterpretable due to containing too few items. Results showed the communalities for the 3 factor model excluding strain were slightly lower than for the 4 factor model; however, there was little difference in between the strength of factor loadings for the two models see Table 7. KMO for the 3 factor model excluding strain items was higher, and fell just below the 0.

Interpretation of the 3 factor exclusion model factors presented 3 distinct factors which explained Two factors were consistent with the 4 factor inclusion model. Factor 1 was indicative of labeling theory, and factor 3 was indicative of interaction theory; however, the second factor did not align with the 4 factor inclusion model. In the 4 factor inclusion model, factor 2 was indicative of control theory; however, in the 3 factor exclusion model, factor 2 contained only two items from differing theoretical perspectives.

Although one item within factor 2 was coded as a control theory item, the factor loading was closely matched to the second item and did not provide strong enough evidence for the factor to be indicative of control see Table 7. These sections were determined by dividing the item statements in categorical groups which were uncovered in the review of the literature to be potential influences affecting truancy. Individual models for each section along with a full 5-factor section model were produced. Exploratory factor analyses using both PAF and PCA extraction were performed on each of the five categorical sections individually.

Table 8 Factor loadings and communalities for school section principal axis factoring and principal component models. Two of the items overlapped between the two models. Table 11 Factor loadings and communalities for friend section principal axis factoring and principal component models. Table 12 Factor loadings and communalities for you section principal axis factoring and principal component models.

As seen in Table 13, the full section model analyzed with principal axis factoring presented five distinct factors explaining Once again, a factor loading cutoff criteria of 0. Two of the five factors aligned with the defined sections of item groupings. Also seen in Table 13, the full section model analyzed with principal component analysis presented one distinct factor explaining Three points of interest were noted regarding the analysis of the full 5 factor PCA model in comparison to the PAF full 5 factor model. First, the PCA model produced only 4 factors in the solution rather than the requested 5 factors. Second, the factor was not as strong, or well defined, as in the factors in the PAF model.

This has been attributed to principal component analysis being weighted more heavily on the first factor than in principal axis factoring as previously stated in the literature. Third, it was noted that the PCA factor structure, or items constructing a factor, was very weak in comparison to the PAF model. Although factor 1 presented 14 items, the remaining factors collectively presented only 4 items — 1 items in factor 2, 1 item in factor 3, 2 items in factor 4, 0 items in factor 5 see Table Factor 1 of the PCA model, did not present an overall distinct theme. Items contained within the factor represented each of the five categorical sections. However, there were no patterns present in the ordering or arrangement of the items within the factor.

Items were representative of both positive and negative aspects, thoughts, and behaviors. Of the remaining factors, factor 2 and factor 3 contained only a single item. Neither of the factors were interpretable. Factor 4 contained 2 items, but still contained too few for it to be interpretable. Factor 5 contained no items, and was, therefore, unable to be interpreted as well. Each of these three measures have the ability to be calculated from a single sample and questionnaire administration. Split-half reliability measures the internal consistency using a split-half method which requires that two equal portions to be compared within any given set of items. There were a few instances of negative reliability values with the split-half reliability method.

A negative reliability is not plausible, yet the split- half method will produce such values due to the estimate calculation requiring equally split halves for proper computation. When halves are unable to be divided equally due to an odd number of total items, a negative reliability is produced. Lambda 2, although less common, is preferred by some researchers since it is believed to be a closer estimate of true reliability. Values are presented for each of the three measures to provide a greater sense of true reliability.

A cutoff criteria for good internal consistency of 0. Assumptions were examined using SPSS regression and explore functions. Examination presented no significant violations. No outliers were present. However, both approximate number of missed days per month and number of recorded tardies were potentially influenced by differing school policies, procedures, and tracking methods, as well as, data collection occurring late in the school year.

Therefore, the approximate number of days missed per month and the number of recorded tardies were determined to not be relevant predictors. Additional regression analysis of the demographic variables excluding these two variables was warranted. The lower value of the adjusted R2 results from R2 being statistically adjusted for the number of predictor variables in the model and sample size. The adjusted R2 decreases every time a predictor variable is added which does not improve the model more than what would be expected by chance.

According to semi-partial squared correlation sr2 , the significant correlation between the number of recorded absences and grades accounted for over one-third of the unique variability contributed to the total R2 value see Table This independent variable accounted for The independent variable gender also presented as significant in the regression; however, this variable was believed to be a suppressor. The suppressor variable, gender, was not correlated with the dependent variable, or a significant predictor when examined without grades. The suppression that was evident in the model has been classified as classic suppression, meaning that the significant independent variable, gender, was not correlated with the number of recorded absences by itself, but was correlated with the other significant independent variable, grades.

Non-significant predictors were age, race, parent s highest level of education, and number of siblings. Multiple regression analysis with forward and backward elimination was also performed using item statements as independent variables predicting absences. The combination of student responses for these eight independent variables accounted for R indicates item was reversed scored. The positive correlation of the items to the number of absences meant that as response scores increase, or as the agreement with the item statements increase, the numbers of absences increase.

The negative correlation of the item statements to the number of absences meant that as response scores increase, the number of absences decrease. Variance shared among the variables in the model was 0. Additionally, the significant demographic predictor variables and the significant item statement predictor variables were combined for multiple regression analysis with both forward and backward elimination. Multiple regression was used to determine if the demographic characteristics of grades and gender along with responses to the aforementioned item statements were potential predictors to number of absences. Discussion Truancy has been discussed as a widespread issue which has reached epidemic proportions Siegel et al. The question is: what underlies truancy? The present study has explored the phenomenon of truancy through the use of an interdisciplinary approach.

According to the knowledge of the principal investigator, this is the first interdisciplinary study regarding truancy. Disciplinary perspectives of criminal justice, psychology, education, and sociology were identified and critiqued, and their subsequent similarities were illuminated to present the interdisciplinary nature of the phenomenon of truancy. As discussed in the review of the literature, previous research shows the diversity of these perspectives in relation to the issue of truancy; however, even with their differences, four common theoretical themes emerged — control theory, interaction theory, labeling theory, and strain theory.

The four hypothesized theories were assessed via a new questionnaire instrument compiled by the principal investigator. The purpose of implementing a new questionnaire was to not only assess the hypothesized theories, but to also capture student perspectives. Students were, on average, fifteen and a half years old, which is in accordance with previous research Kronholz, ; Crabtree, J. Four theories were hypothesized to underlie truancy- labeling theory, control theory, interaction theory, and strain theory. Exploratory factor analysis with both principal axis factoring and principal component analysis extraction were conducted and compared to test the hypothesized theories as underlying constructs of truancy.

Exploratory factor analysis with principal axis factoring extraction conformed more closely to multiple theoretical dimensions underlying the phenomenon through strong factor structures. Multiple regression analysis was performed on both demographic information and item statements which were tested as predictors for the number of days absent from school. Grades and gender were significant in the demographic regression model as predictors. A final multiple regression analysis combined the significant predictor variables from both the demographic and item statement regression models.

Each of the eight aforementioned item statements remained as significant predictors in the final combined model. Item statements were written to represent the underlying theoretical constructs which were discovered as common ground among the four disciplines. According to multiple regression analyses, each theory was represented among the eight significant item statement predictors. Not only do these findings provide evidence of potential predictors of truancy, but also provide supporting statistical evidence for the interdisciplinary nature of the construct.

Overall, the importance of the findings are the discovery of a potential predictive tool which would allow for the identification of students at risk for truancy, and a proactive, rather than reactive, approach to affect the problem. Previous research has lacked insight in examining truancy from an integrated perspective. The present research study fills the need by providing evidence of integrated disciplines as underlying truancy. The information contained herein lays the groundwork for integrated truancy initiatives to be developed.

First, a small sample was obtained which hinders generalizability of the findings. Also, differing school policies and procedures, and the potential for inconsistent reporting hinder the generalizability. Differing school policies, procedures, and reporting methods warrant further investigation. These item statements would require revision for clarification prior to any further study. Third, the present study was conducted late in the school year which may have impacted the sample by not capturing the responses of more truant or at risk students.

Finally, the honesty of the student when responding to the item statements must be considered. Some students may have been less forthcoming in their responses to particular item statements. Future research could benefit from the exploration of truancy as a school-based phenomenon. The differences between urban, suburban, and rural school truancy patterns would aid in establishing targeted, and larger scale truancy initiatives. Also, further study of the discordance with school policies and procedures would provide knowledge and insights to devise and implement core standards across school districts.

Additionally, further research performing test — retest validation of the present study instrument could provide a solid predictive tool for schools to identify students who would benefit from targeted interventions. Ad hoc analysis provided evidence that the exclusion of the strain items marginally improved factorability of the model. The replication of the study with a larger sample size would be beneficial to verify the results for the inclusion and exclusion of strain items, and its effect on the full models. Further research of strain would provide greater insight on the issue. The interdisciplinary nature of the present study yields evidence of the interrelatedness of criminal justice, psychology, education, and sociology, in regards to truancy, thus, supporting the requirement for collaboration.

Collaborative action would not only reduce the impact of truancy on the justice and education systems, but would also reduce the impact of truancy on the lives of truant and at risk students. The availability of a predictive tool like the one in the present study would allow schools to have a proactive stance against truancy rather than a reactive one. Conclusion Four common theories — control, interaction, strain, labeling - were discovered during review of the literature to underlie truancy. Statistical evidence, through the use of exploratory factor analysis with principal axis factoring, presented 3 of the 4 theories — control, interaction, labeling — as affecting truancy.

These findings provide support for the existence of interdisciplinary gaps between the disciplines of criminal justice, education, psychology, and sociology which have prevented an effective method s for combatting the truancy epidemic to be discovered. The three common theoretical dimensions bridge together the four disciplines signifying interdisciplinarity. The interdisciplinary nature of truancy warrants the need for an interdisciplinary response.

The lack of an interdisciplinary response will continue to be disadvantageous in affecting the phenomenon. A comprehensive approach to disruptive behaviors in the classroom and home. The International Journal on School Disaffection, Barusch, A. Foundations of social policy. Third Edition. The common factor model and exploratory factor analysis. In Confirmatory factor analysis for applied research Bryant, F. Principal components analysis and exploratory and confirmatory factor analysis.

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Research in Education, November , Repko, A. Interdisciplinary research: process and theory. Rocque, M. Journal of Criminal Law and Criminology, 2 , Sander, J. School policies academic achievement and general strain theory: applications to juvenile justice settings. Schmalleger, F. Criminology today: an integrative introduction. Fifth Edition, Columbus, OH: Prentice Hall. Schwalbe, M. The sociological examined life: pieces of the conversation. Sheppard, A. Development of school attendance difficulties: An exploratory study. Pastoral Care, 9, Emotional and Behavioural Difficulties, 12 4 , Siegel, L.

Juvenile delinquency: theory, practice and law. Ninth Edition, , Belmont, CA: Thomson Learning. Smith, J. Interpretive phenomenological analysis: theory, method and research. Spencer, A. School attachment patterns, unmet educational needs and truancy: a chronological perspective. Remedial and Special Education, 30 5 , Journal of Youth and Adolescence, 37, Studsrod, I. Upper secondary school students perceptions of teacher socialization practices and reports of school adjustment. School Psychology International, 33 3 , Sullivan, C. Adolescent risk behavior subgroups: an empirical account. Journal of Youth and Adolescence, 39, Tabachnick, B. Using Multivariate Statistics. Fifth Edition. Thompson, B. Exploratory and confirmatory factor analysis: understanding concepts and applications.

Upchurch, C. Verleger, R. International Journal of Pschophysiology, 87, Walkey, F. Demystifying factor analysis: how it works and how to use it. Bloomington, IN: Xlibris. Williams, B. Exploratory factor analysis: a five-step guide for novices. Journal of Emergency Primary Health Care, 8 3 , Williams, L. Investigating truancy in secondary schools Unpublished doctoral dissertation.

University of the Incarnate Word, San Antonio. Wilson, V. British Educational Research Journal, 34 1 , Homework purpose scale for middle school students: a validation study. Middle Grades Research Journal, 6 1 , Young, A. Journal of Youth and Adolescence, 38, Zhang, D. Truancy offenders in the juvenile justice system: a multicohort study. Behavioral Disorders, 35 3 , This study will be conducted with the 10 th grade students at Holmes High School, and has been approved by its superintendent, Ms.

Lynda Jackson. Participants in the study will complete a survey consisting of questions about student school experiences, activities, and individual feelings. The printed survey will be given during school and will take approximately 15 to 20 minutes to complete. After a participant number is assigned, any identifying information will be destroyed in accordance with University policy. I will have no way to connect any of the information provided back to your child.

Even though I will not be able to connect any of the information to your child, responses, attendance records, and GPA will continue to be treated as confidential information. I will secure all research data on a password protected computer which is accessible only by me. Participation in this research study is completely voluntary. Any insights gained will be used to benefit further research endeavors in the area of school truancy. Should you decide at a later time that you would prefer your child not be included, you may withdraw your permission at any time. If you have any questions or concerns, please contact me at or at my email address, spauldingj2 nku.

You may reach my faculty advisor, Dr. Bill Attenweiler, at or at attenweilerb nku. I appreciate your help. This study will be conducted with the 10th grade students at Holmes High School, and has been approved by its superintendent, Ms. As a participant in the study you will complete a survey compiled of questions about your school experiences, activities, and individual feelings. Your survey responses will be confidential. Your information will be assigned to a participant number in order to match school data with survey responses. After being assigned a participant number, any identifying information will be destroyed in accordance with University policy. I will have no way to connect any of the information provided back to you.

Even though I will not be able to connect any of the information to you, responses will continue to be treated as confidential information. Your decision to participate or not will have no effect upon your school records, grades, or how you are treated at school. Should you decide at a later time that you would prefer not to be included, you may withdraw at any time. If you have questions about your rights as a participant in the research study, you may contact Philip Moberg, Ph. Please indicate if you agree to the use of your responses for the research study by marking one of the blanks below, and by signing this form.

Degree Thank you for participating! To renew, submit a request in writing to the IRB Administrator prior to the expiration date. If no changes have been made to the research project, simply complete the first two-pages of the IRB Application with signatures, mark the box labeled Co ti uatio , atta h ost re e t CITI s ores a d o se t for a d su it to the IRB Ad i istrator i of the Lucas Administrative Center. Evidence for Social Control Theory tends to focus on three problem areas that are correlated with higher crime rates.

These are: Absentee parents; Truancy; Unemployment. Found that offenders were more likely to come from poorer, single parent families with poor parenting and parents who were themselves offenders. This study suggests that good primary socialisation is essential in preventing crime. He argues that this is the single most important factor in explaining youth offending. He argues that children need both discipline and love, two things that are often both absent with absent parents. The problem is increasingly threatening some inner-city schools, with teachers claiming that the influence of gang culture has soared over the past three years.

If you like this sort of thing, then you might like my Crime and Deviance Revision Notes — 31 pages of revision notes covering the following topics:. This site uses Akismet to reduce spam. Learn how your comment data is processed. Skip to content A consensus theory which argues that crime increases when the bonds attaching the individual to society weaken. According to Social Control Theory, truancy is an indicator of low social-attachment, and thus a predictor of criminal behaviour Politicians of all persuasions tend to talk in terms of social control theory.

Criticisms of Social Control Theory Some crimes are more likely to be committed by people with lots of social connections — e. Interactionism — Middle class crimes are less likely to appear in the statistics — In reality the attached middle classes are just as criminal. By focussing on the crimes of the marginalised, the right wing elite dupe the public into thinking we need them to protect us from criminals whereas in reality we need protecting from the elite This may be a case of blaming the victim — We need to look at structural factors that lead to family breakdown poverty, long working hours, unemployment. Parent deficit does not automatically lead to children becoming criminals. Leave a Reply Cancel reply. This website uses cookies to improve your experience.

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Telep CW, Weisburd D What is known about the Truancy Theory of Truancy Theory practices in reducing crime and disorder? Mundfrom, Truancy Theory. Article Google Scholar Perkins, Truancy Theory. Hirschi Truancy Theory a different approach Truancy Theory delinquency by Truancy Theory not Truancy Theory causes Truancy Theory to deviate from Truancy Theory by the laws, Truancy Theory rather Truancy Theory causes Brave New World Social Class Analysis not to deviate.