This chapter entails an indication of the manner in which data for analysis will be collected, arranged, sorted and analysed in order to generate information for formulation of conclusions and recommendations. Data relevant to the aspects of ASD are collected and analysed to help in evaluating various aspects of cognitive behavioral interventions adapted and their effectiveness. These models are deemed sufficient to ensure reliability of the responses and the outcomes of analysis.
The objectives of the study shall be achieved using triangulation design through the convergence model as explained by Creswell and Plano Clark (2011). Triangulation mixed methodology is a design that can be explained as a single-phase research design where both qualitative and quantitative methods are implemented within the same time frame. While the triangulation design has various approaches, the convergence triangulation model is employed when it is desired that the quantitative and qualitative data should be merged concurrently (Morgan, 2007). This approach allows for thorough comparison of data hence brings out a better understanding of the phenomenon being investigated.
The participants in the study will comprise teachers and students from special education elementary and middle schools. The teachers that participate in the study are randomly selected based on initial fulfilment of a specific selection criterion. First, the teacher participating must be a teacher to children aged between 11 and 14 who exhibit ASD. Secondly, the teacher must at least be a holder of a bachelor’s degree in education in addition to possessing current teacher certification. Then thirdly, with respect to research ethics, the teacher must only participate on consenting to do so. A total sample of 4 teachers will be randomly selected for the study based on the criterion stated above. Two of the randomly selected teachers will tasked with delivering the intervention model.
The other group of participants will comprise students who will also be randomly selected based on initial fulfilment of selection criterion for participation. A total sample of 26 students will be engaged in the study where 8 shall be the control group while the other 8 will be the group being followed.
The research employs a quasi-experiment approach where different approaches will be used to sample students and teachers. While random assignment is used for teachers, the sampled students are not randomly assigned. However, selection of students for participation in the research study is random where 4 students are randomly selected from each of the 4 teachers’ classrooms. In this study, the research applies the qualitative and quantitative research strategies in order to capture all aspects of the data collected. Quantitative research strategy implies that the research relies on pre-established assumptions to determine the causations or relationships between the variables determined in the study. Through this model, hypotheses are tested in order to assess the impact of variability in the data.
Qualitative research strategies applied in the study are used to introduce the social aspects of analysis in the data. In addition, the study draws extensively from the salient strength of qualitative research that helps in focusing on contexts and meaning of the topic under investigation as part of day to day human lives and experiences.
Analysis based on existing theory is performed, with elements of past experiences playing a role in the analysis of data and this is the junction that integrates qualitative approach with quantitative approach with viewed from the perspective of initial analysis of data (Bryman, 2006)). In addition, qualitative research offers in-depth analyses on opinions from participants measured against existing theories and information drawn from the literature review, with this information originating from assertions in the questionnaires, oral interviews and other forms of observations. According to Dawson (2002), use of the two models should contribute to comprehensiveness of the analysis as opposed to contradictions in assertions.
Data collection is a primary aspect of any research project. Data collected for the research provides the basis for assertions and conclusions since it represented the views and perceptions of the sample on the subject matter of the study. Data for research project can either be primary or secondary. Primary data is collected essentially for the current project. Collection of such data occurs through questionnaires and other forms of data collection method, involving the direct or indirect contact with the respondents. Since it is collected for the current study, it is the most relevant form of data. In essence, the parameters and provisions for collection of this data are aligned with aims and objects of the study.
The integration of various data types that formerly comprised qualitative and quantitative data is achieved through data merging where the analysis of quantitative data and the analysis of quantitative data are carried concurrently and a relationship drawn out within the analysis (Creswell & Plano Clark, 2011). The data analysis was performed in order to transform the collected data into information for decision-making. Tabulation and presentation of data was performed through the use of statistical packages such as Excel, with analysis through SPSS 16.0. Data was tabulated into graphs and tables in order to enable analysis to be performed, and the analysed data presented through numerical, graphical and explanatory models.
All categorical items with reference to customer behaviour were transformed to an ordinal scale that ranges from the minimum value (for instance, “Very Unlikely”) to the maximum value (for instance, “Extremely Likely”). Items or questionnaires that did not receive response were coded as missing values. Scale values were therefore to be calculated as the average or mean of the single items. All items are assumed consistent with characteristics of a normal distribution. Nevertheless, all questionnaires were successfully filled.
The qualitative data is coded appropriately to allow for possibility of handling it using quantitative techniques and make data integration easier. For this reason, appropriate segments are demarcated within the qualitative data and then coded. Highly structured data (for example, open-responses from respondents) were coded without subjecting it to any further segmentation. This makes such data analysable using both qualitative and quantitative techniques (Denzin and Lincoln, 2000).
Descriptive statistics and correlation analysis were performed to evaluate whether there is significant relationship between customer satisfaction behaviour and the various consumer attributes. The relationship between the various variables was measured through the Pearson product-moment correlation coefficients (Grinnell & Unrau, 2007).
All the p-values were two-tailed. The p-values less than 0.05 are considered significant given that the significance level will be 0.05 or 5%. The values will be given as mean and standard deviation and the data will be calculated using SPSS Version 16 and Microsoft Excel.
Data collection was performed through online means, thus eliminating the face to face contact with the respondents. However, assurances were made to ensure that the identity of the individuals were not revealed, since the names and other demographic aspects were based on general limits, such as age groups. Similarly, the responses to the questionnaire were to be used for academic purposes and for this survey only. The responses to the questions were also based on the perceptions of the individuals and not a representation of the views and reality at the store. As a result, any assertions based on this study should be considered under the existing parameters in order to validate their reliability.
3.6 Philosophical Basis for Research Design
Rationale for choice of mixed method design as the best research design for the study was arrived at based on several aspects of the research study that call for a mixed methodology approach in order to be effective. First off, the study encompasses multilevel perspectives that that are intended to solve research questions that require real-life contextual understandings. For this to be achieved effectively, Creswell (2003) admonitions that a mixed method approach is essential to ensure that the advantages of employing multiple methods such as in-depth interviews and survey questionnaires are utilized and built upon. Creswell further points out that taking the mixed method approach enables the research study to draw from the strengths of each approach; that is, strengths of qualitative approach and quantitative approach as shown in the table below:
|Quantitative Research||Qualitative Research|
|Advantages||Allow for a broader study and enhancing the generalization of the results
Allow for greater objectivity and accuracy of results.
Employs prescribed procedures
Personal bias can be avoided
|Provides depth and detail
Creates openness: encouraging people to expand on their responses can open up new topic areas not initially considered
Simulate people’s individual experiences
Attempts to avoid pre-judgments
|Gather a much narrower dataset
The context of the experiment is ignored
The results are limited
Should involves a large sample of the population, this can cost more.
The development of standard questions by researchers can lead to ‘structural’ bias and false representation
|More difficult to generalize
Difficult to make systematic comparisons
There may be more subjectivity concerned in analysing data and the researcher may have more influence over the results.
Not easy to conclude the reliability and validity of data
Data overload may need a lot of time to analyse!
Recorded interviews would cost more time and money
Source: Adopted from http://www.learnhigher.ac.uk/analysethis/main/quantitative1.html
In addition to the philosophical bases provided above for choice of research design, there is yet another basis for the choice of mixed methodology approach as identified by Greene (2007). Greene points out that generally researchers and investigators collect diverse types of data and these can only be integrated through the use of mixed method where the strengths of different data types are brought together to form a coherent data output (Greene 2007).
Nevertheless, as the various data types need to be integrated, there comes with it the difficulty of choosing how to integrate the data. This research study employs the integration method postulated by Creswell and Plano Clark (2011) in which they advise that while there are three basic approaches to merging data (merging, embedding and connecting data), merging data is used as it provides a chance for maximal use of both data collected.
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