This experiment evaluates the positive consequences of participating in a body satisfaction program in older people, by examining body satisfaction before and after intervention and comparing experimental and nonexperimental groups. This experimental mixed design makes it possible to isolate the effect of treatment from the manipulation effect by intergroup comparison and other variables related to individual differences by within subject comparison. this is the most effective methodology for determining causal relationships in behavior sciences and evaluating whether a psychotherapeutic intervention produces real and solid improvements.
Start by opening the statistical software and go to file menu, select new and click on the data icon. Open variable view and create a statistical variable for each variable listed in table one in the manuscript. Open data view and fill in the data of the pre and post measures of the body shape questionnaire or BSQ test for each participant.
Similarly, fill in the data from the demographic and attributive questionnaire. After filling in the data, go to transform compute variable and assign a number in target variable gap from the popup up menu, then select the pre-treatment variable from the type and label menu, move it to numeric expression gap and click on the subtraction icon on the calculator. Select the post-treatment variable from the type and label menu and move it again to numeric expression gap.
Finally, hit the OK'tab to create a variable with the difference between the pre and post BSQ measurement. After digitization of the data, look at the reliability by selecting reliability analysis from scale in the analyze menu and moving the pre and post-treatment BSQ measurements used in the experiment to the reliability analysis dialogue box. Click on statistic and choose intraclass correlation coefficient, then click the options two-way mixed and consistency.
Finally, click on the OK'icon to generate the desired output. Run the descriptive analysis by selecting analyze menu and descriptive statistics, then click frequencies. After the output, select analyze descriptive statistics and descriptive.
To specify the descriptive statistics of the quantitative variables, go to split file in the main menu and choose the categorical variable to be analyzed in the popup menu. Select the option organize output by groups'and click OK.To conduct a paired samples Student's T-test on the body image data collected before and after taking part in the two conditions, go to analyze menu, choose compare means and in the paired samples T-test dialogue box, put BSQ pre-treatment and BSQ post-treatment as variable one and two. Specify the paired samples Student's T-test according to each categorical variable, by selecting split file from the main menu and choosing the categorical variable to be analyzed in the popup box.
Then click the tab organize output by groups and hit OK.Repeat this process for each nominal variable. To see the effect of each program conduct one way ANOVA by selecting compare means in analyze menu to access the one way ANOVA dialogue box. In the box put the variables BSQ pre and post-treatment and the pre-post difference in the dependent list as well as the experimental condition variable as the factor.
For repeated measures ANOVA analysis, go to general linear model in analyze menu. In the repeated measures dialogue box, assign a name in the within subject factor name. Then put two as the number of levels and BSQ in the measure name.
Finally, click on define to switch to the variable selection box. Within the popup menu, select tabs within subject's variables, between subject's factor and all the socio-demographic variables as covariates. Finally, click on model and select full factorial.
Go to options to choose estimates of effect size. Repeat the process to build custom terms and use the icon by'to combine the variable condition with all the sociodemographic variables. In the representative analysis, the size of effect in the experimental and control groups before and after participants'enrollment, along with the difference between two moments were displayed with a paired samples test.
The output of the paired samples test showed that there was significant improvement in body image in participants of the IMAGINA program compared to that of the control condition. The analysis of intergroup effect with a one way ANOVA revealed non-significant mean differences between pre and post conditions, concluding that the test design is robust. Also a significant improvement in BSQ in the pre-post difference, indicated good performance of the BSQ test.
Findings from the multivariate test demonstrated a statistically significant inter, intragroup interaction effect. Pointing to the effectiveness of the IMAGINA body satisfaction program. The effect of intervening variables like gender, marital status and season of year was analyzed in body satisfaction differences.
Male subjects were observed to be more satisfied with their physical appearance than women. However, the difference between the measure of the BSQ before and immediately after the intervention was statistically significant for both genders after taking part in the IMAGINA program. Participants within a relationship, were found to be more unhappy with their physical appearance in the pre and post-treatment condition.
But this also improved their body satisfaction more significantly during their participation in IMAGINA. The season of year did not significantly affect individuals in the control group, but it affected those in the experimental group. The improvement was higher for metropolitan individuals than in countryside individuals in the experimental condition.
The most critical step in this protocol is to replicate the same experimental conditions in experimental and control conditions to isolate the effect generated by the treatment.
