Copyright Malvin Porter, Jr. 2010 - acumen - The University of ...
Copyright Malvin Porter, Jr. 2010 - acumen - The University of ... Copyright Malvin Porter, Jr. 2010 - acumen - The University of ...
A Chi-Square (Ҳ 2 ) test on independence was used to determine whether or not there was a dependency between children’s Intellectual Ability as perceived by teachers and children’s CBVS Action Choices and Justification Choices. Are there significant differences in the proportion of children’s responses on the CBVS Justification Choice subscales (Prosocial/Care, Aggressive/Retribution, Justice/Fair) and CBVS Action Choice subscales (Prosocial, Aggressive)? A Chi-Square (Ҳ 2 ) test of independence was used to determine whether children’s Action Choices (e.g., Prosocial, Aggressive) were dependent on their Justification Choices (e.g., Prosocial/Care, Aggressive/Retribution, Justice/Fair). Tests for the difference in proportions were performed to determine if there were interactions between variables. Tests for the difference in proportions were performed by participant children’s Gender (e.g., male, female), Story Character Form (e.g., bystander, victim), and Story Form of Victimization (e.g., physical, relational). Does teacher-report CBS Behavior with Peers (e.g., Prosocial With Peers, Aggressive With Peers) relate to children’s CBVS Action Choices (e.g., Prosocial, Aggressive)? Subscale means and standard deviations for the CBS and CBVS subscales were computed by averaging children’s additive scores across all of the items contained within a subscale sum. The internal consistency of each subscale was estimated with Cronbach’s alpha. Alphas for the two CBS subscales were moderately high to high in magnitude. A stepwise backwards logistic regression model was used to determine if teacher-report scores on Prosocial With Peers and Aggressive With Peers subscales on the CBS predicted children’s Prosocial and Aggressive Action Choices on the CBVS. The backwards elimination analysis was used as a means of comparing the CBS continuous rated data with the CBVS nominal categorical data, which started out with CBS 66
Prosocial with Peers and CBS Aggressive with Peers as the predictors of CBVS Action Choices in the model. At each step the predictors in the model were evaluated and eliminated if they met the significance criterion of p > .05 for removal in order to least reduce the R 2 (Hosmer & Lemeshow, 1989; Pedhazur, 1997). Do the teacher reports of children’s social behavior with peers as assessed by the CBS Prosocial With Peers subscale and the Aggressive Behavior With Peers subscale of the Child Behavior Scale (CBS, Ladd & Profilet, 1996) relate to the children’s CBVS Justification Choices that are coded as either Justice/Fair, Prosocial/Care, Aggressive/Retribution? A multinominal Logistic Regression model based on CBS subscales (e.g., Prosocial with Peers, Aggressive with Peers) was used to predict the CBVS Justification Choice subscales (e.g., Prosocial, Aggressive/Retribution, Justice/Fair) using Justice/Fair as the baseline variable. Multinomial logistic regression is used when the dependent variable, Action and Justification Choices, in question is nominal (a set of categories which cannot be ordered in any meaningful way) and consists of more than two categories (Prosocial vs. Aggressive actions, Prosocial/care vs. Aggressive/Retribution vs. Justice/Fair justifications) (Harrell, 2001). Multinomial logistic regression is appropriate in cases where the response is not ordinal in nature as in ordered logistic. In contrast, ordered logistic regression is used in cases where the dependent variable in question consists of a set number (more than two) of categories which can be ordered in a meaningful way while multinomial logistic regression is used when there is no apparent order. The multinomial logistic model assumes that data are case specific; that is, each independent variable (e.g., CBS Prosocial with Peers and CBS Aggressive with Peers) has a single value for each case. The multinomial logistic model also assumes that the dependent CBVS variables cannot be perfectly predicted from the independent CBS variables for any case. As with other 67
- Page 29 and 30: actively involved, or as defenders
- Page 31 and 32: who are victimized themselves), and
- Page 33 and 34: Bullying often occurs within the co
- Page 35 and 36: manipulate social relationships to
- Page 37 and 38: Longitudinal studies of children en
- Page 39 and 40: Bully Bullying also damages the bul
- Page 41 and 42: for the nonbully/nonvictim. From co
- Page 43 and 44: Being victimized has been correlate
- Page 45 and 46: Linking Social Support and Bullying
- Page 47 and 48: (Bandura, 1986, 1991). Bandura (198
- Page 49 and 50: Social-cognitive learning theory ha
- Page 51 and 52: influenced and guided by informatio
- Page 53 and 54: ignore him or her, or that it is be
- Page 55 and 56: (Dodge, 1980a; Dodge & Frame, 1982)
- Page 57 and 58: during a child’s early years and
- Page 59 and 60: The reader is reminded that the pur
- Page 61 and 62: CBVS is scored by comparing the dif
- Page 63 and 64: Other research suggests that childr
- Page 65 and 66: their behaviors, which may explain
- Page 67 and 68: Aggressive) and Justification Choic
- Page 69 and 70: Cohen-Posey, 1995; Coloroso, 2003;
- Page 71 and 72: children’s arrival, answered ques
- Page 73 and 74: esponse to either being bullied or
- Page 75 and 76: Peers (7 items) and the Aggressive
- Page 77 and 78: Children’s Social Experience Ques
- Page 79: Standard Deviations, ranges, and Cr
- Page 83 and 84: Choices (e.g., Prosocial, Aggressiv
- Page 85 and 86: CHAPTER IV: RESULTS The purpose of
- Page 87 and 88: for Prosocial Actions and victim ch
- Page 89 and 90: Table 4 Psychometric Properties for
- Page 91 and 92: all four stories. Bystander charact
- Page 93 and 94: Victim character / relational victi
- Page 95 and 96: Table 8 Psychometric Properties for
- Page 97 and 98: Prosocial/Care justification or Jus
- Page 99 and 100: Figure 1. Chi-Square (Ҳ2) Test for
- Page 101 and 102: Figure 2. Chi-Square (Ҳ2) Test for
- Page 103 and 104: Table 11 CBVS Chi-Square (Ҳ 2 ) Te
- Page 105 and 106: Of the 187 responses to Prosocial/C
- Page 107 and 108: of children’s Prosocial Action Ch
- Page 109 and 110: Table 15 Predicting Action Choices
- Page 111 and 112: Table 18 Predicting Action Choices
- Page 113 and 114: participant child variable such as
- Page 115 and 116: Figure 4. Chi-Square (Ҳ2) Test for
- Page 117 and 118: A test for the Difference in Propor
- Page 119 and 120: Figure 5. Chi-Square (Ҳ2) Test for
- Page 121 and 122: Table 24 CBVS Test and Confidence I
- Page 123 and 124: Therefore, I reject the null hypoth
- Page 125 and 126: Figure 6. Chi-Square (Ҳ 2 ) Test f
- Page 127 and 128: Table 28 CBVS Test and Confidence I
- Page 129 and 130: Justice/Fair) on bystander and vict
Prosocial with Peers and CBS Aggressive with Peers as the predictors <strong>of</strong> CBVS Action Choices<br />
in the model. At each step the predictors in the model were evaluated and eliminated if they met<br />
the significance criterion <strong>of</strong> p > .05 for removal in order to least reduce the R 2 (Hosmer &<br />
Lemeshow, 1989; Pedhazur, 1997).<br />
Do the teacher reports <strong>of</strong> children’s social behavior with peers as assessed by the CBS<br />
Prosocial With Peers subscale and the Aggressive Behavior With Peers subscale <strong>of</strong> the Child<br />
Behavior Scale (CBS, Ladd & Pr<strong>of</strong>ilet, 1996) relate to the children’s CBVS Justification Choices<br />
that are coded as either Justice/Fair, Prosocial/Care, Aggressive/Retribution? A multinominal<br />
Logistic Regression model based on CBS subscales (e.g., Prosocial with Peers, Aggressive with<br />
Peers) was used to predict the CBVS Justification Choice subscales (e.g., Prosocial,<br />
Aggressive/Retribution, Justice/Fair) using Justice/Fair as the baseline variable. Multinomial<br />
logistic regression is used when the dependent variable, Action and Justification Choices, in<br />
question is nominal (a set <strong>of</strong> categories which cannot be ordered in any meaningful way) and<br />
consists <strong>of</strong> more than two categories (Prosocial vs. Aggressive actions, Prosocial/care vs.<br />
Aggressive/Retribution vs. Justice/Fair justifications) (Harrell, 2001). Multinomial logistic<br />
regression is appropriate in cases where the response is not ordinal in nature as in ordered<br />
logistic. In contrast, ordered logistic regression is used in cases where the dependent variable in<br />
question consists <strong>of</strong> a set number (more than two) <strong>of</strong> categories which can be ordered in a<br />
meaningful way while multinomial logistic regression is used when there is no apparent order.<br />
<strong>The</strong> multinomial logistic model assumes that data are case specific; that is, each independent<br />
variable (e.g., CBS Prosocial with Peers and CBS Aggressive with Peers) has a single value for<br />
each case. <strong>The</strong> multinomial logistic model also assumes that the dependent CBVS variables<br />
cannot be perfectly predicted from the independent CBS variables for any case. As with other<br />
67
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