TPC-Journal-V5-Issue3

The Professional Counselor /Volume 5, Issue 3 371 (p. 39). To our knowledge, no assessment currently exists to classify specific IPV typologies. Other popular assessments of IPV exist, such as the Revised Conflict Tactics Scale (CTS; Straus, Hamby, Boney-McCoy, & Sugarman, 1996), but the CTS results do not classify types of IPV behavior with considerations for the victim or the victimizer. The IJS has potential to distinguish between degrees of violence severity, and has been used in studies to differentiate between lower levels and higher levels of violence aggression (e.g., Friend, Bradley, Thatcher, & Gottman, 2011). Scores in the current study ranged from 15–64 ( M = 27.02). Alpha reliabilities for participants in the current study were .92. Results Preliminary Analysis Prior to data analyses, we conducted preliminary analyses to test for assumptions, outliers and missing data. The ACV, IJS, and UTR did not meet the assumption of normality, with K-S p values falling below .001. The ACV and IJS resulted in a positive skew, while the UTR resulted in a negative skew. The distributions indicated that most respondents did not report favorable attitudes toward violence, the overall existence of relationship inequality (risk for IPV) or perceptions of partners using technology in an unhealthy manner. This finding is consistent with the mean IJS score (27.02), indicating minimal risk of violence in the sample. Thus, we did not implement any transformation procedures. Potential outliers existed for the ACV and IJS scores. However, examination of the 5% trimmed mean indicated minimal influence on the mean score. Furthermore, these scores represented participants reporting different attitudes and experiences with IPV. Sixteen participants had missing data points. We created a dummy variable to compare some demographics for those who had complete data versus those who did not. No differences existed between those with and without missing data on age and credit hours taken during the semester of survey administration. We determined that the data were likely missing at random, although it is possible data were missing due to some variable not measured. We used hot deck imputation to address the missing variables (Andridge & Little, 2010; Myers, 2011). Hot deck imputation calculates an average score on an identified outcome variable by matching the score to like variables in the sample (i.e., donor variables). We used participants’ gender, grade level and current relationship status as the donor variables. SPSS averaged the score for matching participants and imputed. Matches existed for 13 of the 16 missing scores. Hot deck imputation provides less bias than mean imputation, and is deemed a better overall solution than the oft-used listwise deletion (Andridge & Little, 2010; Myers, 2011). Primary Analysis To begin testing the research questions, we conducted Pearson correlations to examine the relationships between demographics and other constructs of interest (i.e., PSS, IJS, ACV and UTR). Pearson correlation indicated (a) a significant positive correlation between gender and IJS scores, (b) a significant negative correlation between gender and UTR scores, (c) a significant positive correlation between PSS scores and IJS scores, (d) a significant positive correlation between the ACV and IJS scores and (e) a significant negative correlation between UTR scores and IJS scores (See Table 2 for correlations). A scatterplot matrix indicated that (a) increases in stress correlate to increases in intimate justice scores, (b) more favorable attitudes toward couple violence correlate to increases in intimate justice scores; and (c) lower perceived use of technology (i.e., more responses of “no”) correlates with higher intimate justice scores.