The data collected from Behavioral Treatment Services (BTS) was manually entered to help the researcher analyze the data. The data was organized into columns and described using the following nominal variables as column headings: treatment outcome, trauma cohort, ethnicity, and race. education level, and referral source. The trauma cohort was coded as 0 for no trauma group and 1 for trauma group. Age and education level were entered using the numerical value pulled from BTS archives with U indicating any age or education level which was unspecified. Treatment outcome was coded as either 0 for unsuccessfully completed or 1 for successfully completed treatment. Ethnicity was coded using alphanumerical values: W for White, H for Hispanic, B for Black or African American, NA for Native American or Alaska Native, A for Asian, O for other, and U for unspecified. Referral source was coded as: C for court diversion, D for department of corrections, I for Intervention, Inc. (ICCS), and U for unspecified. Once the data was coded, the researcher attempted to clean the data by spot-checking the entered data against the data collected from BTS archives. At the beginning of the data cleaning process, the researcher realized that no identifiers had been included to help reference the data against the BTS archives for cleaning. Since the data had no identifiers to appropriately clean the data, the data had to be collected a second time. The data was collected a second time from BTS archives and subsequently cleaned through spot-checking. With data coded, entered, and cleaned the researcher will conduct descriptive statistical analyses on the data.
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Five frequency (f) tables will be constructed for univariate analysis. Standard deviation (SD) will be calculated for treatment outcomes. The standard deviation will allow the researcher to examine how much the individual values of the tables differ from the mean and other values (Royse, 2010). Percentage (%) will be calculated for all nominal variables to help visual the data through historgrams. Mean (M) will be calculated for age and education level which will help determine the average of each nominal variable. Mode will be calculated for age, education level, ethnicity and referral source. From the frequency (f) distribution table and data table, bar charts and pie graphs will be generated to help visual the data. Part II: Sample Characteristics From the data analysis, we can begin to ascertain the characteristics of the sample population from the Residential Dual Diagnosis Treatment (RDDT) program. Twenty participants were sampled for the current study. All twenty participants had a clinical diagnosis of Post-Traumatic Stress Disorder (PTSD), as diagnosed by a Master’s level clinician. The participants were referred to and discharged …show more content…
The mode of the participants showed that White was the largest represented race at 65%. The second most represented ethnicity in the study was Hispanic or Latina at 10% and Native American was the third most represented at 5%. The analysis showed that 20% of the participants did not have an ethnicity specified as represented in Figure 2. Figure 3 shows the breakdown of participant characteristics by education level. From Figure 3, we can see that 6 participants, or 30%, of participants had a high school education, 20% had 11th grade education or less, 25% of participants had some college education, and 25% of participants did not specify an education
Five frequency (f) tables will be constructed for univariate analysis. Standard deviation (SD) will be calculated for treatment outcomes. The standard deviation will allow the researcher to examine how much the individual values of the tables differ from the mean and other values (Royse, 2010). Percentage (%) will be calculated for all nominal variables to help visual the data through historgrams. Mean (M) will be calculated for age and education level which will help determine the average of each nominal variable. Mode will be calculated for age, education level, ethnicity and referral source. From the frequency (f) distribution table and data table, bar charts and pie graphs will be generated to help visual the data. Part II: Sample Characteristics From the data analysis, we can begin to ascertain the characteristics of the sample population from the Residential Dual Diagnosis Treatment (RDDT) program. Twenty participants were sampled for the current study. All twenty participants had a clinical diagnosis of Post-Traumatic Stress Disorder (PTSD), as diagnosed by a Master’s level clinician. The participants were referred to and discharged …show more content…
The mode of the participants showed that White was the largest represented race at 65%. The second most represented ethnicity in the study was Hispanic or Latina at 10% and Native American was the third most represented at 5%. The analysis showed that 20% of the participants did not have an ethnicity specified as represented in Figure 2. Figure 3 shows the breakdown of participant characteristics by education level. From Figure 3, we can see that 6 participants, or 30%, of participants had a high school education, 20% had 11th grade education or less, 25% of participants had some college education, and 25% of participants did not specify an education