The previous chapter presents the theoretical framework for the research study as well as the research design and methodology employed in carrying out the study. The respective research questions and hypotheses were outlined. Reviews of the instrument development and screening procedure employed in developing the construct were outlined. The sample plan and data collection methods were presented and the planned hypotheses tests were provided.
The purpose of Chapter Four is to present the analysis of the data and the resultant findings from the tests conducted on each of the respective hypotheses. An overview of the data collection, survey ...view middle of the document...
The purpose of this study was to identify what, if any, factors impacted service quality from the clients’ perspective. This information could then be used to determine whether the overall impact was positive or negative as it impacted the organizational structure of human service agencies and providers’ level of job satisfaction. This latter, it was hypothesized, had direct bearing on the quality of service provided from the perspective of the recipients of these services and therefore, was a valid measure of how well agencies were fulfilling their mission and purpose.
The results of this study are presented in three sections. The first section provides an analysis of the demographic data collected from each of the surveys. The second section is an interpretation and analysis of the survey data for each section of the two surveys conducted. The final section includes a sequential interpretation and analysis of data collected in the context of the five research questions that form the basis of this study.
Cronbach's α (alpha) is a coefficient of internal consistency. It is commonly used as an estimate of the reliability of a psychometric test for a sample of examinees. It was first named alpha by Lee Cronbach in 1951, as he had intended to continue with further coefficients.
Internal consistency is usually measured with Cronbach's alpha, a statistic calculated from the pairwise correlations between items. Internal consistency ranges between zero and one. A commonly-accepted rule of thumb is that an α of 0.6-0.7 indicates acceptable reliability, and 0.8 or higher indicates good reliability. High reliabilities (0.95 or higher) are not necessarily desirable, as this indicates that the items may be entirely redundant. The goal in designing a reliable instrument is for scores on similar items to be related (internally consistent), but for each to contribute some unique information as well.
Missing Value Analysis
Proper handling of missing values is important in all statistical analyses. Improper handling of missing values will distort analysis because, until proven otherwise, the researcher must assume that missing cases differ in analytically important ways from cases where values are present. That is, the problem with missing values is not so much reduced sample size as it is the possibility that the remaining data set is biased. The imputation of values where data are missing is an area of statistics which has developed much since the 1980s
Results and discussion
In order to test the first set of hypotheses (H1.1 to H1.5), a paired ``t'' test has
been carried out to check for differences between service quality and customer satisfaction with respect to the five factors. The results are summarized in Table III. The results indicate that service quality and
customer satisfaction vary significantly with respect to all the five factors. This underscores that fact that service quality and...