My chapter 5 needs some heavy editing. <br /><br />Pasting the project purpose below so you can see it..also pasted my chapter 4 so you can see the results, which is already edited.<br /><br /><br /><br /><p><strong>Project Purpose: </strong></p><p>Studies have shown that college students have a tendency to overestimate the amount of alcohol their peers drink.  These misperceptions, or incorrect perceptions, may cause college students to engage in abnormal drinking behavior they wouldn’t normally engage in due to the fact that they think what they are doing is “normal.” People are more likely to engage in a behavior when they think it is normal or acceptable and, if they knew the behavior was not normal, their tendency to engage in such behavior would be reduced. “Well everyone’s doing it so I should do it to.”</p><p>This study will examine whether presenting normative information about college drinking behavior to college students—as well as how that information is presented—has any effect on their perceptions of college drinking behavior. The researcher will assign participants to one of three groups. One group will receive information on typical drinking behavior with the data anchored in abstinence/low drinking (i.e., 55% of students consume 1 drink or less per week) and the other will have the data anchored in heavy drinking (i.e., 45% of students drink heavily). Finally, a third group, which will serve as a control group, will not receive any normative data. This will allow to the researcher to first determine whether the presentation of normative data has any effect on college students’ misperceptions of drinking behavior. Also, it will inform the researcher whether the manner in whichthe data are presented has an effect on these misperceptions. Essentially, could outreach efforts benefit from presenting information in a way that focuses on the majority of students who do not engage in problematic drinking or only the minority of students who do engage in problematic drinking?</p><p>Discovering a low-cost, minimally invasive way to clarify misperceptions about drinking behavior would be useful in helping colleges across America reduce the incidence of problematic drinking. If students realize that heavy drinking is not the norm, they may be less likely to engage in such behaviors. This study attempts to clarify misperceptions about college drinking so students will realize that problematic drinking is not the norm.</p><p><strong>Chapter 4: Results</strong></p><p>A variety of tests were used to analyze and verify the hypothesis. All tests had a significance threshold set at ? = .05. Data was inputted and analyzed in SPSS.</p><p><strong>Descriptive Statistics</strong></p><p>            A total of 336 surveys were completed and 327 were completed fully, yielding an attrition rate of about 2.7%. All participants reported gender information and only 334 reported their academic year. The gender data revealed an almost equal number of male and female participants with 173 males and 163 females participating. The table below illustrates the respective percentages of each gender followed by a presentation of the gender proportions in Figure 1.</p><p>Table 1.</p><p> </p><table width="678"><tbody><tr><td colspan="6" width="678"><p><em>Participant Gender</em></p></td></tr><tr><td colspan="2" width="162"><p> </p></td><td width="125"><p>Frequency</p></td><td width="95"><p>Percent</p></td><td width="152"><p>Valid Percent</p></td><td width="143"><p>Cumulative Percent</p></td></tr><tr><td rowspan="3" width="71"><p>Valid</p></td><td width="91"><p>Male</p></td><td width="125"><p>173</p></td><td width="95"><p>51.5</p></td><td width="152"><p>51.5</p></td><td width="143"><p>51.5</p></td></tr><tr><td width="91"><p>Female</p></td><td width="125"><p>163</p></td><td width="95"><p>48.5</p></td><td width="152"><p>48.5</p></td><td width="143"><p>100.0</p></td></tr><tr><td width="91"><p>Total</p></td><td width="125"><p>336</p></td><td width="95"><p>100.0</p></td><td width="152"><p>100.0</p></td><td width="143"><p> </p></td></tr></tbody></table><p> </p><p> </p><p>Figure 1.</p><p> </p><p>The data gathered from the respondents regarding their academic year revealed the highest percentage of respondents reported being Seniors in college (N=90) followed by Juniors (N=90), Sophomores (N=89), Freshman (N=40) and 5+ Year Seniors (N=17). Table 2 presents a breakdown of the reported frequency of participants in relation to each college year, followed by a visual illustration of the resulting proportions in each category in Figure 2.</p><p>Table 2.</p><table width="678"><tbody><tr><td colspan="6" width="678"><p><em>Participants by College Year</em></p></td></tr><tr><td colspan="2" width="210"><p> </p></td><td width="113"><p>Frequency</p></td><td width="86"><p>Percent</p></td><td width="138"><p>Valid Percent</p></td><td width="131"><p>Cumulative Percent</p></td></tr><tr><td rowspan="6" width="86"><p>Valid</p></td><td width="124"><p>Freshman</p></td><td width="113"><p>40</p></td><td width="86"><p>11.9</p></td><td width="138"><p>12.0</p></td><td width="131"><p>12.0</p></td></tr><tr><td width="124"><p>Sophomore</p></td><td width="113"><p>89</p></td><td width="86"><p>26.5</p></td><td width="138"><p>26.6</p></td><td width="131"><p>38.6</p></td></tr><tr><td width="124"><p>Junior</p></td><td width="113"><p>90</p></td><td width="86"><p>26.8</p></td><td width="138"><p>26.9</p></td><td width="131"><p>65.6</p></td></tr><tr><td width="124"><p>Senior</p></td><td width="113"><p>98</p></td><td width="86"><p>29.2</p></td><td width="138"><p>29.3</p></td><td width="131"><p>94.9</p></td></tr><tr><td width="124"><p>5th year +</p></td><td width="113"><p>17</p></td><td width="86"><p>5.1</p></td><td width="138"><p>5.1</p></td><td width="131"><p>100.0</p></td></tr><tr><td width="124"><p>Total</p></td><td width="113"><p>334</p></td><td width="86"><p>99.4</p></td><td width="138"><p>100.0</p></td><td width="131"><p> </p></td></tr><tr><td width="86"><p>Missing</p></td><td width="124"><p>System</p></td><td width="113"><p>2</p></td><td width="86"><p>.6</p></td><td width="138"><p> </p></td><td width="131"><p> </p></td></tr><tr><td colspan="2" width="210"><p>Total</p></td><td width="113"><p>336</p></td><td width="86"><p>100.0</p></td><td width="138"><p> </p></td><td width="131"><p> </p></td></tr></tbody></table><p> </p><p>Figure 2.</p><p>      </p><p> </p><p><strong>Statistical Analyses</strong></p><p>            Use of parametric statistical testing was deemed inappropriate, due to the fact that the dependent variable was ordinal and not continuous. In addition, distribution of this ordinal variable was not approximately normal, and there were only three response options resulting in a low-resolution scale not suitable for parametric testing.</p><p>            Given that the dependent variable was ordinal, and all dependent variables were categorical, ordinal logistic regression was selected as the most appropriate test. Binomial logistic regression was also considered, but rejected because dichotomizing the ordinal dependent variable would result in losing some of the meaning in the data.</p><p>Because the dependent variable was ordinal and all independent variables were categorical, the statistical test employed was ordinal logistic regression. Variables were recoded to determine reference groups. For gender, female was set as the reference group. As a result the statistical analyses indicate the difference for males compared to females. For condition, the control group that received no information statistics on drinking was set as the comparison group, such that dummy coded variables for the drinking and abstaining conditions were analyzed for their differences from the control group. When the study was designed, three potentially significant independent variables were identified: (a) experimental condition, (b) gender, and (c) class year (e.g., Freshman, Sophomore). Data analysis (Spearman’s rank-order correlations) revealed that college year was not related to the dependent variable and was excluded from regression analyses for hypothesis testing. Two ordinal logistic regressions were performed; the first with gender and experimental condition as independent variables predicting perceptions. The second was identical except for the addition of an interaction term gender*condition.</p><p>     The first ordinal logistic regression analysis yielded several significant findings. Results for the overall regression models were not strong although there is some strength. Only the model for perceptions of heavy drinking nationally was significant (?<sup>2 </sup>= 6.097, <em>p</em> = .014). In omnibus tests of the independent variables, experimental condition was a significant predictor of heavy drinking perceptions at the national level (Wald ? <sup>2 </sup>= 12.146, <em>p</em> = .002), moderate drinking perceptions for college (Wald ? <sup>2 </sup>= 37.745, <em>p</em> < .001) and national (Wald ? <sup>2 </sup>= 47.196, <em>p</em> < .001) levels, and light drinking perceptions for friends (Wald ? <sup>2</sup>=15.880, p<.001), college (Wald ? <sup>2 </sup>= 22.116, <em>p</em> < .001), and national (Wald ? <sup>2 </sup>= 34.425, <em>p</em> < .001) levels. This indicates that the manipulation was successful, and the statistics displayed to the participant had an effect on their reported perceptions. Upon further inspection, the source of significant differences based on experimental condition was typically the abstaining statistics experimental condition. As one example, for moderate drinking perceptions at the college level, the drinking condition did not differ significantly (Wald ? <sup>2 </sup>= .004, <em>p</em> = .947) from the control group, but the abstaining condition did (Wald ? <sup>2 </sup>= 5.843, <em>p</em> = .016). This trend of significance for abstaining held true for all other scenarios with the exception of moderate drinking among friends, where neither condition variable was significantly different from the control. Gender was also a significant predictor of two of the nine dependent variables, heavy drinking among friends (Wald ? <sup>2 </sup>= 17.11, <em>p</em> < .001), and light drinking among friends (Wald ? <sup>2 </sup>= 4.799, <em>p</em> < .028). These results indicate that males reported that heavy drinking was more typical among their friends and that light drinking was less typical, consistent with our previous correlation finding.</p><p>The second ordinal logistic regression retained gender and experimental condition as predictors and included an interaction term for gender*condition. Results changed considerably indicating that the effects of the experimental conditions were not the same for both genders. All nine model fits improved (lower p-values, higher pseudo <em>R</em><sup>2)</sup>. In the omnibus tests for condition and gender*condition, results indicated that the interaction was very important. The interaction term was a significant predictor of five of the nine dependent variables. For many of the dependent variables, where the omnibus test for experimental condition was significant, it was no longer significant after the addition of the interaction term. As examples of this change in significance when the interaction term was added, for moderate drinking at the college level, condition was highly significant (Wald ? <sup>2 </sup>= 37.745, <em>p</em> < .001) in the first model, but not significant (Wald ? <sup>2 </sup>= 3.949, <em>p</em> = .139) in the second, while the interaction term was a strong predictor (Wald ? <sup>2 </sup>= 13.748, <em>p</em> < .001). Similarly condition was a significant predictor for all three levels of light drinking in the first model, but none of them in the second. The interaction term was significant for two of the three. By examining cell counts (see Table 1), it appears that experimental conditions had much more influence on the scores of males than females.</p><p><strong> </strong></p>