Data Analysis is the processing of data collected to make meaningful information out of them (Sounders, Lewis and Thornhill, 2009). This is necessary as raw data convey little meaning to most people. After data was obtained through questionnaires, interviews, observations and through secondary sources, it was prepared in readiness for analysis by editing, handling blank responses, coding, categorizing and keyed in using SPSS statistical package (version 20). Burns and Grove (2003) define data
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analysis as a mechanism for reducing and organizing data to produce findings that require interpretation by the researcher. Quantitative information is usually analyzed through statistical procedures. Statistical analyses cover a broad range of techniques, from simple procedures that we all use regularly (e.g., computing an average) to complex and sophisticated methods. Although some methods are computationally formidable, the underlying logic of statistical tests is relatively easy to grasp, and computers have eliminated the need to get bogged down with detailed mathematical operations (Polit and Beck, 2003).
Factor analysis was used to establish the appropriateness of the questionnaire constructs. Specifically factor loadings were used to establish the weights of the various statements on extracted factors. Before the factor analysis was conducted, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was conducted to determine whether adequate correlation exists between the individual items contained within each of the sections of the questionnaire. A KMO statistic, an associated Bartlett’s p-value and an Anti-image correlation statistic are determined when using this test.
In this study the following odd ratio regression / logistic regression equation was used to test the specific research objectives:
Objective one – Establish technology influences in accounts receivables management in the hotel industry in Kenya. The binary logistic regression equation was applied to establish the effect of determinants on accounts receivables management,
53 Odds of AR = Accounts receivables.
X1= Vector of Technology
Objective two - Determine the effects of size of an organization in accounts receivables management in the hotel industry in Kenya. The binary logistic regression equation was applied to establish the effect of determinants on accounts receivables management,
Odds of AR = Accounts receivables. X2 =Vector of Size
Objective three - Establish the effects of marketing channels in accounts receivables management in the hotel industry in Kenya. The binary logistic regression equation was applied to establish the effect of determinants on accounts receivables management,
Odds of AR = Accounts receivables.
X3 = Vector of Marketing
Objective four – Determine the effects of management structures in accounts receivables management in the hotel industry in Kenya. The binary logistic regression equation was applied to establish the effect of determinants on accounts receivables management,
Odds of AR =β0+β1X1+€
Odds of AR =β0+ β2X2 +€
54 Odds of AR = Accounts receivables.
X4 = Vector of Management structure
Objective five - Determine policies influences in accounts receivables management in the hotel industry in Kenya. The binary logistic regression equation was applied to establish the effect of determinants on accounts receivables management,.
Odds of AR =β0+β5X5 +€
Odds of AR = Accounts receivables.
X5 = Vector of Policies
General Objective - To establish the determinants of accounts receivables management in the hotel industry in Kenya. The binary logistic regression equation was applied to establish the effect of determinants on accounts receivables management,
Where:
1. Odds of AR = Accounts receivables
2. {βi; i=1,2,3,4,5} = The coefficients representing the various independent
variables.
3. {Xi; i=1,2,3,4,5} = Values of the various independent variables
(covariates).
Odds of AR =β0+β4X4 +€
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4. € is the error term which is assumed to be normally distributed with mean zero and constant variance.
Hosmer and Stanley (2000) emphasize that regression methods have become an integral component of any data analysis concerned with describing the relationship between a response variable and one or more explanatory variables. The data that was obtained from the questionnaires was primarily qualitative and was analyzed to identify the most important and statistically significant determinant of accounts receivables management variable or variables that have impacted most on hotel performance on accounts receivables management. According to IBM (2010), logistic regression is useful in situations where there are more than two independent variables and the dependent variable is categorical.
IBM Base (2011), states that a paired samples t-test compares the means of two variables for a single group. The t-test of significance was used to test whether the change in the dependent variables identified is statistically significant. F-test was used to analyze the variances while chi-square test was used to analyze the observations in this study.
Qualitative content analysis and quantitative data analysis was used to analyze the data collected from the field. Qualitative content analysis is a method of analyzing written, verbal or visual communication messages (Cole, 1988). Content analysis as a research method is a systematic and objective means of describing and quantifying phenomena (Sandelowski, 1995). In qualitative content analysis data was coded which represents the operations by which data are broken down, conceptualized and put back together in new ways. This involved three coding steps namely, open
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coding, axial coding and selective coding. Open coding refers to close examination of the data so that phenomena may be named and categorized. Axial coding refers to refining and differentiating the categories generated in open coding. Selective coding is a continuation of axial coding at a higher level of abstraction. It aims at laying bare the core category around which the other categories can be arranged.
Quantitative data was analyzed using various statistical methods for measuring central tendencies including mean, median and mode as well as measures of dispersion including standard deviation. Quantitative data was subjected to statistical tests including the t-test, the F-test and the Chi-square test to establish the levels of significance and the strengths of the relationships. Multiple linear regression was also used to analyze quantitative data to establish the causal effect of one variable upon another and establish the relationship between the various variables.
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