Family Treatment Model and the Group Treatment Model of Disorders - Assignment Example

Family treatment model and the group treatment model of disorders What are the similarities and differences between the outcomes of family treatment model and the group treatment model of disorders? Abstract A number of scholars have argued that families and groups are central to the development of behavioral and emotional youth problems. Researchers hold that the relationship between the risk and the vulnerability is purely strengthened during the early stages of life via a number of negative interactions between the children and those who surrounding him/her. In families, the most difficult interactions often persist throughout the life of a child and adolescent stage. Lack of positive parenting skills, poor management of the families and care giving which is functional have been associated with delinquency amongst the youths. Therefore scholars have used family support to facilitate the prediction of positive adjustment during adolescent. Given the fundamental influence of the family and groups on the life of a child, research has proved that there is a potential benefit of including the family and groups when treating high risk youths. Workings with group and family system have proved to be the most effective way of dealing with drug abuse and delinquent youths. The study will use both primary and secondary to analyze the similarities and differences between the outcomes of family treatment model and the group treatment model of disorders Statement of the Problem To find answers for the question, the research has to identify appropriate samples to include in the research. Appropriate sample population for this research will include adolescents with conduct disorder involving violation of rules. Ad discussed in the DSM-IV, for a condition to qualify as a conduct disorder, it must have occurred with a period of six months. The serious violation of rules to investigate in the samples include, a) At least two overnight runaways from home while living with parents b) Staying out of home at night despite prohibition by parents c) Unpermitted absenteeism from school To identify the samples, the researcher will contact correctional centers or counseling resource centers. In these facilities, the researcher will able to find the victims or individuals admitted for treatment due to repeated violation of institutional rules. Contacting the facilities will also enable easy reach to counselors who employ the target treatment models to treat clients with conduct disorders. Selection of the participant samples will rely on the volition of every individual (Bordens & Abbott, 2014). There will be no compulsion or enticement of any person to participate. This statement is to mean that there will be payments or promise of any gains to induce the samples into participating in the research. To select the clients or victims of conduct disorders, counselors working in the correctional and counseling resource centers will have to disclose or allow access to the individuals. Since doing so without consent of the clients under treatment is unethical, the researcher will have to rely on two options. One of the options is for the counselors to consult with the clients to know who is willing to participate in the research. The clients who express their willingness to participate will have consented for disclosure of name and details to the researcher before the actual contact. Another option is for the counselors to share details of clients treated in the past and expressed willingness to sensitize communities and conduct campaigns to encourage children and adolescents to respect rules set at homes and school (Bordens & Abbott, 2014). Undoubtedly, a person who involves in sensitization of communities about conduct disorders after having recovered from similar conditions have little or no problem being disclosed. This view is due to the fact the past clients involving in sensitization will have to present selves as examples to give first-hand information to the audiences to believe the facts presented about the conditions. Prior to beginning the actual study, it is important to obtain informed consent from the target samples. The researcher will consider written and signed consents of the clients to validate their willingness to participate in the research. Counselors agreeing to support the research must witness the clients giving consent for their participation. This move is to eliminate any doubt about the possibility of use of financial or bribery to convince the victims of the disorders to participate in the research (Margolis & Zweben, 2011). To avoid ethical issues arising in the course of the research, the researcher will have to inform the clients and the supporting counselors about the essences of the study, methodologies, and any implications of the study. Dependent variable in the study will be the possibility of relapse among the individuals treated through the family and group models of treatment. To measure the possibility of relapse, the research will consider behaviors of the participants and testimonies of the counselors. Relapse is the common variable used by the counselors of conduct disorders to measure effectiveness of different techniques of treatment employed. Techniques that are likely to register higher relapse among client are usually unreliable and inappropriate for use in treating the particular conditions (Schonfeld & MacFarland, 2015). Testimonies from counselors and clients are important tools for measuring the achievements of the two treatment techniques in addressing disorder problems. Behaviors act as good assessment platform for measuring relapse. Each conduct relates to certain unique behaviors that can indicate process of relapse. The researcher will look for behavioral clues related to violations of rules in school and home. For assessment the reliability and validity of items of measurement, a researcher will consider those who will receive treatment. In our study, adolescents with conduct disorder involving violation of rules were considered. The number of the adolescents who were treated will be recorded and the data will be subjected to statistical analysis (Bordens & Abbott, 2014). The results obtained from the analysis will be compared against the existing theoretical studies conducted in the past on psychological therapeutics. Background information It is axiomatic to argue that family and groups play a fundamental role during the treatment of any health problem including drug abuse. Despite their effectiveness of the two therapies in the treatment, the question that has been asked by a number of professionals is whether the two have been applied to help deal with the adolescent disorders. Wenzel, (2013) holds that there is no psychopharmacological and psychological treatment methods that have demonstrated effectiveness compared to family models. For the adolescent, it is proved that family based models are reasonable in dealing with such cases. Group care is mainly intended to be a last resort to respond to the psychological problems that cannot be easily addressed in a family based setting. With the growing number of family and home based care options, the group options have fallen into disrepute. Both group and family based approaches present different similarities with few differences in their applications. According to Schonfeld, & MacFarland, (2015) some of the existing similarities include; Treatment outcomes- The involvement of both the family and groups have seen a positive association with an upsurge in the rates of engagement for entry into treatment. Both methods have also witnessed a reduction in the rates of dropout during treatment with long term outcomes. Both methods have also resulted into a long term treatment relationships with the affected adolescent youths. Both methods ensure that there is a quick and long term recovery of the affected person. When the family members and the friends realize their level of participation in ensuring that the problem of drug abuse or any other condition has been dealt with, it is likely that the youth will have a long recovery from the effects. The other similarity is that when the groups and family members are involved in the treatment, it is likely that the family will focus on a larger complex issues not that the effect of maybe substance abuse. It is likely that the family can understand something that other people might not understand about the affected people. Both the affected people and the families’ member can collaborate to offer the much needed support. The last similarity is the intergenerational impact. Both the models have the potential of reducing the impact of the possible recurrence of the problem to other groups. When the problem is not dealt with, it is likely that there will be recurrence problem in future. Apart from the similarities, it should be noted that there are significant differences that exist in the two models. Group model is mainly applied as the last option when all other methods have failed. It means that its level of effectiveness may be question. There is also difference in terms of level of participation and involvement during the entire treatment process. Purpose of the study The research question intended to form the basis of research is: What are the similarities and differences between the outcomes of family treatment model and the group treatment model of disorders? To study this question effectively and come up with reliable answers, the research will employ the sociometry of the nonexperimental research design. According to Bordens and Abbot (2014), sociometry refers to the type of nonexperimental research design characterized with a researcher identifying and assessing interpersonal relationships in a group of people. The technique becomes effective for studying and describing interactional patterns within social networks, work relationships, and in assessing peers solicited in taking drugs. The choice for sociometry is due to its appropriateness for effective study of the question. One element that the research is going to consider is exploring the question is behavior of the subjects. Already, it is crucial to seek for consent of the participants and the counselors. So, it is not easy to apply other designs like ethnography and natural observation since the two can only be appropriate in cases where consent is unnecessary. In fact, seeking for client’s consent while to applying ethnography or natural observation is enough to compromise the purpose of a research (Stone-Romeo, 2011). Case history, archival research, and content analysis are irrelevant and inapplicable to the design of the study. The research will majorly apply interviews and observations to gather and record findings. This expression is to dispute any possibility of employing case history, content analysis, and archival research. From the analysis, sociometry becomes the automatic choice since it involves interaction with participants who have knowledge of the purpose of the research (Cillessen & Mayeux, 2004). As discussed by Bordens and Abbott (2014), sociometric study has to have participants evaluate one another along certain dimensions. Sociometry fulfills the purpose and planned design of the research that will have counselors and clients under treatment or treated from conduct disorders as the participants. As initially intended, counselors will help in evaluating the conducts of the clients studied to obtain understanding of the relationships in the outcomes between individual and family models of treating conduct disorders. While using the sociometric design of nonexperimental research, the researcher will consider using stratified and random sampling techniques. Stratified sampling technique will be important in classifying the individuals as either treated through individual or family therapy (Bordens & Abbott, 2014) In each category, the researcher will further stratify the samples to isolate those treated from ADHD from ones treated from substance abuse. The researcher will then apply random sampling to pick a maximum of three participants from each category formed through the second start. Sampling will be important to avoid problem that may emerge due to a large population of clients willing to participate in the research (Bordens & Abbott, 2014). For each category, the researcher will initiate roles that will bring members close, and where possible start friendship. Through closeness and/or friendship, members of each category will able to understand mutual behaviors. This activity will take two weeks before the actual study. The researcher will then request members of each category to describe the behaviors of the colleagues since the day of knowing each other to the time of the study. To ensure confidentiality and avoid fear, the researcher will separate the participants by positioning them five meters away. The researcher will also request counselors who treated the clients to record changes in behaviors since the day of admission into the contacted correctional centers. The process will also involve the researcher asking individual participants to compare their status throughout the treatment and recovery periods. The measurement technique will involve comparison based on effectiveness of the individual and family therapy methods of treating conduct disorder (Thorndike, 1913). With effectiveness, the researcher will analyze and categorize positive and negative behavior changes against the treatment methods. The researcher will further compare the results given by the participating clients against the ones given by the counselors with specificity of each treatment technique. Research question 1. Between the home therapy and group therapy, which is more effective in the treatment of conduct disorder? 2. Do home and group therapies eliminate disorders? Hypothesis a. Home therapy is more effective in the treatment of conduct disorder than the group therapy. b. Both the group and home therapies eliminate the conduct disorders. Research Timeline The timeline for this study will be determined by the nature of experimentation conducted. In this research, experimentation can be conducted on the previously treated conduct disorder patients, or on the ones under the treatment. However, for the research to utilize the available time wisely, it would be advisable to conduct the research on the already treated conduct disorder patients. In this case, the research period would be approximately three months. Data collection method The main method of collecting data in this research will be direct observation. The researcher will observe the patients who form the sample group, to identify any conduct disorder. The instances of the observed conduct disorder (if any) will be recorded. Research design This research will use the between-subjects design (Kenneth & Bruce, 2014). This research design will allow the averaging of the data collected, as well as the analysis. Justification Contrary to the requirements of the ordinary scientific studies, this type of research doesn’t require any control group. For this reason, most of the experimental designs will hardly fit in this study. The most appropriate experimental design for this kind of study would be direct observation, since the symptoms of the conduct disorders are observable (Kenneth & Bruce, 2014). The observation method supports the use of between-subjects research design. Sampling technique In this research, the target population is composed of the patients who underwent conduct disorder treatment, either through the group or home therapy methods. Due to the nature of the target population, each member should be given an equal opportunity of forming the sample group. This is because the treatments that each of the members underwent were similar. In this research therefore, the sampling technique will be simple random sampling. This technique will give each of the members equal chances of being selected to form the sample group. The sample size will be made up of 30 participants, in an equal number, across the two treatment methods. 15 participants will be randomly selected to as representatives, depending on the method of conduct disorder they were subjected to. Experimentation The nature of this study is comparative in nature (Thorndike & E. L. 1913). For this reason, as opposed to the ordinary scientific researches, this study would not require splitting the sample group into control and experimental groups (Lacort & M. O, 2014). . The study compares the effectiveness of the home and group treatments for the treatment of conduct disorders. For this reason, in this study, the observations will be made on the persons treated under each category of treatment. The effectiveness of each treatment option will be determined by considering the number of patients depicting the conduct disorder symptoms under the specified percentages. The percentages of conduct disorder will be categorized under the percentages; 0-1%, 2-5%, 6-10%, 11-20%, 21-60%, 61-90%, and 91-100%. The experimentation process will begin with data collection. The data collection will be based on the observable conduct disorders in the treated patient. The patients will be grouped into two categories, depending on whether they were treated via home or group therapy. Observations on the conduct disorders in the patients (if any) will then be recorded. Measurement method To measure the results of the study, the distribution analysis on the data will be done, to identify the distribution of the recorded symptoms, based on the percentage scales identified in the previous section. For instance, if there are members in the scale 0-1% in the home therapy category, It would be an indication that the members have either completely healed or the observable conduct disorders are negligible. Likewise, if there lie many members in the 91-100% category under group treatment, this will be an indication that, there still exists many observable conduct disorders in the sample group. These will be used as the basis for comparison between the two categories of therapy. Instrumentation and measurement Data measurement is term used to refer the organization of data in order to visualize into graphs and analyze the data. We shall use a quasi-experimental design to evaluate conditions disorders in the adolescent that include, running away from home, school absenteeism without permission and alcoholism (Kenneth S. Bordens, 2014). To evaluate our study, we shall use quasi- experiment which allows the quasi-independent variable to be evaluated under natural conditions (Kenneth S. Bordens, 2014). The time series design will be used in a longitudinal study in that the data will be semi-annually collected; strict agreements will be drawn between the parents of the sample population. The prototype/willingness model (PWM) is a concept founded on opportunities in risky behavior among the adolescents; thus the adolescent willingness to engage in such in behavior is influenced the belief of the peer pressure the adolescent is living in (Brian E. Armenta, 2015). Hence, as the study involves behavioral studies over a period of time, time series design best suit in our study. The measures of study will revolve between the ages 10, 11 and 12. Hence before the data collection the families will provide us enrolled adolescent students in their families, this will define our sample population. The families will be visited and informed in detail the objective of our study and that the information collected will be handled with as strictly confidential (Kenneth S. Bordens, 2014). Families agreed will be visited bi-weekly for a period of six months, only target adolescent will be interviewed on camera. We shall use different levels of measured including the nominal measurement to measure the absenteeism and alcoholism between the males and females. Ordinal measurements will measure the how many times the students were absent in school and number of times the adolescent has indulged in alcohol in the last two months. We will inspect data collected in readiness for analysis by cleaning it, re-modelling the data and transforming it where applicable and only the data that have certain parameters that are void of bias-ness and manipulation will be considered for analysis. We shall use the following measures of spread in descriptive statistics to describe the main features of the data collected. We shall use histograms with a normal curve to visualize the frequency of the data, scatter plots to evaluate the correlation patterns between the variables, bar graphs and box-whisker graphs (Kenneth S. Bordens, 2014). As the data is qualitative, other measures will be mode and median. Table 1 Hypothetical data Age Gender Absent (Days) Alcohol (Days) 11 M 3 3 13 F 2 2 15 M 4 4 12 M 2 2 15 F 1 2 12 F 2 3 15 M 3 1 12 M 2 2 12 F 2 2 12 F 2 3 Table 2 Descriptive statistics Age Absent (Days) Alcohol (Days) Mean 12.9 2.3 2.4 SD 1.52 0.82 0.84 Median 12 2 2 Mode 12 2 2 Skewness 0.68 0.81 0.39 The hypothetical data show that the average age of adolescent = 12.9 with absent at school on average = 2.3 and consuming alcohol on mean = 2.4. The data is normally distributed with a positive skewness, with a measure of dispersion of SD = 1.52, 0.82 and 0.84. To find the effects of gender differences among the adolescents and compare the difference in ages and factors that influence the absenteeism among the adolescents, other factor that influence the behavior of the adolescents in regards to gender and studies should be evaluated (Heike Eschenbeck, 2007). Hence, we shall use 3 x 2 ANOVA to evaluated the statistical significance of the findings, this as the variables will have a quantitative attribute. F-tests will be carried out on main effect, and interactions between the factors with the follow-up tests shall be carried out to measure hypothesis and its statistical significance (Kenneth S. Bordens, 2014). Data Analysis Results We can create a substantial statistical analysis based in the hypothetical data that is provided. Owing to the nature of the data, it will be advisable to deploy the use of means and averages to create an inferential aspect of the data for instance; Percentage groupings of the symptoms Family therapy (15 subjects) Group therapy (15 subjects) 0-1% 10 0 2-5% 1 0 6-10% 0 7 11-20% 3 3 21-60% 0 2 61-90% 0 3 91-100% 1 0 Mean family therapy= 15/7 Mean family therapy = 2.142 Therefore, it is clear that the average group therapy with regards to family therapy is 2.142 subjects. It is also notable that there was a higher prevalence in the 0-1% cluster as shown in the above table. Graphically, this information can be presented as; From the above, the trends of the subjects and therapies can be seen with respect to the grouped percentages. A flat line is evident where there are no participants in the group therapy. Additionally, one can easily look at the figures and the upper limits of the participants involved in each therapy. Statistical Explanations of the results The above illustrations can be explained using inferential statistics. In inferential statistics, one can use the normal distribution as a particular theoretical model that will aid in the establishment of various inferences regarding a given sample. These assumptions will enable one to establish categorically the possible, likely values of the mean provided that the sample mean is realized. Apart from the sample mean, one can also deploy the use of the z-statistic, the student’s t, the F ratio, and the chi-square distribution that help describe the sampling distributions of the relative statistics. With the aid of these procedures, one can determine if a statistical value is large, larger or largest. Noteworthy, inferential statistics tend to go beyond the traditional description of statistical data. In this regard, one can quickly determine whether the observable differences are reliable with close references to the sample at hand. Conversely, the proper understanding of inferential statistics will enable a statistician to make clearly empirical decisions regarding the viability of the null hypothesis. This determination should go in line with the subsequent mitigation of the rejection of the null hypothesis in the event where the fact is real. This phenomenon gives rise to the Type I error. Categorically, the null hypothesis will show that there is no actual difference among the treatments chosen (Lacort, 2014). By classification, inferential statistics can be divided into parametric and non-parametric equations. The parametric tests, the ANOVA and the t-tests, are aligned with the assumptions surrounding the samples. A good example is when these tests assume that the sample distributions of a given means from a specified data set are homogeneous within various cell variances. Additionally, parametric tests are mostly used in the event where the data is either scaled or has uniform ranges. Subsequently, whenever any data set violates the set of assumptions outlined by the concept of parametric tests- then a non-parametric test will be considered. This method covers the chi-square distribution or the Mann-Whitney U test. By simplicity, these methods are arguably simpler to compute as compared to the parametric approaches. Notably, this approach is limited in terms of applications and terms of statistical power. Moreover, the non-parametric tests cannot be applicable in the event where there are higher-order factorial designs. Noteworthy, there are two factors that contribute to a statistical significant effect. These factors are the size of the differences between the averages and the subsequent variability among the scores. If the variability among means is too high, one would not be able to establish a statistical significance. On the other hand, one can get a subtle difference that will eventually lead to a moderate variability effect. Categorically, the measure of the effect size can help an individual to give an empirical assessment of how strong the treatment differences are in relation to the within-treatment of the variability within a sample. From intensive statistics, it can be suggested that some measure of the effect size can be included among the p-values incorporated in any statistical piece of work. It goes without mention that one should consider the power of the statistical test before coming up with any empirical solutions and subsequent assumptions. Clearly, in the event where a statistician misses finding a statistical significant, there it will be in order to conclude that there are differences in existence. It is also important to note that whenever there are no evident differences, the test could have been less sensitive (Abbot, & Bordens, 2014). From the analysis, it is clearly evident that the larger the sample, the more compelling the statistic. By explanation, larger sample sizes cover a wider scope of data and are more representative than the samples collected from relatively smaller samples. Therefore, it is imperative that a statistician should establish the exact and optimal sample size in a bid to give the desired results. In order to get the most optimal sample size, one will be obliged to specify the expected magnitude of the specified treatment effect, the error terms, and the desired power of the differences. The expected magnitude of the treatment effect and the estimate of the error variance can be established using the pilot data. While focusing on the Type I errors, an alpha level of .05 is the threshold limit. 0.05 is highly preferred since is depicted a more reasonable compromise between the Type I and the Type II errors (Spiegel, & Stephens, 2014). Whenever any data set is deemed abnormal, data transformation methods will be deployed. Unusual data is characterized by colossal numbers and figures that do not meet the assumptions of outlined by a statistical test. Noteworthy, the transformation of data is not used in inferential statistics is affected significantly by the effects of the moderately severe violations of the statistical assumptions. Additionally, the assumptions should be used sparingly since they tend to change the description of the data used in any statistical study. For instance, one would use the square root when the cell means and variances are related as shown: X’ = X1/2 References Bordens, K. & Abbott, B. (2014). Research Design and Methods a Process Approach. 9th ED. New York; NY: McGraw-Hill Education. Cillessen, A. N., & Mayeux, L. (2004). Sociometric status and peer group behavior: Previous findings and current directions. In J. B. Kupersmidt, K. A. Dodge, J. B. Kupersmidt, K. A. Dodge (Eds.) , Childrens peer relations: From development to intervention (pp. 3-20). Washington, DC, US: American Psychological Association. doi:10.1037/10653-001 Kenneth, B., & Bruce, B. 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Research strategies in industrial and organizational psychology: Nonexperimental, quasi-experimental, and randomized experimental research in special purpose and nonspecial purpose settings. In S. Zedeck, S. Zedeck (Eds.) , APA handbook of industrial and organizational psychology, Vol 1: Building and developing the organization (pp. 37-72). Washington, DC, US: American Psychological Association. doi:10.1037/12169-002 Thorndike, E. L. (1913). The measurement of differences and of changes. In , An introduction to the theory of mental and social measurements (2nd ed.; rev. and enl.) (pp. 127-140). New York, NY, US: Teachers College Press. doi:10.1037/10866-009 Wenzel, A. (2013). Relapse prevention and completion of treatment. In , Strategic decision making in cognitive behavioral therapy (pp. 203-216). Washington, DC, US: American Psychological Association. doi:10.1037/14188-011 Read More