# Analysis of variance anova was used to test the

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power. Analysis of variance (ANOVA) was used to test the significance of the model. R 2 was used in this research to measure the extent of goodness of fit of the regression model. The multiple linear was used to estimate the coefficient was as follows: Y= β 0 + β 1 X 1 + β 2 X 2 + β 3 X 3 + β 4 X 4 + ε Y = Represents the dependent variable, Project Implementation β 0 = Intercept of regression line β 1 - β 4 = Partial regression coefficient of the Independent Variables X 1 = Project Mission X 2 = Project Resources X 3 = Management X 4 = Project Implementation ε = error term or stochastic term. 4. DATA ANALYSIS RESULTS AND DISCUSSIONS Response rate: High response rate guarantees that the findings are representative of the target population. Emore (2007) notes that a response rate is the extent to which the collected data takes care of all the sample items, a ratio of actual respondents to anticipated number of persons who respond to the study. Questionnaires were self-administered whereby a total of 134 questionnaires were given out by the researcher to respondents. One hundred and thirty six (122) questionnaires were completely filled, returned and used for analysis in this study. This meant that the active sample was 122 respondents and
ISSN 2394-9694 International Journal of Novel Research in Humanity and Social Sciences Vol. 5, Issue 4, pp: (53-69), Month: July - August 2018, Available at: Page | 62 Novelty Journals this represented a response rate of 91% percent of the sample size which fell within a large sample size. Table 4.1 presents the percentage of response rate of the respondents. According to Kothari and Gang, (2014) a response rate of 50% is adequate for analysis and reporting; a rate of 60% is good and a response rate of 70% and over is excellent; therefore, this response rate was adequate for analysis and reporting. Table 4.1: Questionnaire Response Rate Frequency Percentage Response 122 91% Non-Response 12 9% TOTAL 134 100% Validity Analysis: Factor analysis was deployed to check on the validity of the constructs. Kaiser-Mayor-Oklin measures of sampling adequacy (KMO) & Bartlett’s Test of Sphericity is a measure of sampling adequacy that is recommended to check the case to variable ratio for the analysis being conducted. In most academic and business studies, KMO & Bartlett’s test pl ay an important role for accepting the sample adequacy. While the KMO ranges from 0 to 1, the world-over accepted index is over 0.5. Also, the Bartlett’s Test of Sphericity relates to the significance of the study and thereby shows the validity and suitability of the responses collected to the problem being addressed through the study. For Factor Analysis to be recommended suitable, the Bartlett’s Test of Sphericity must be less than 0.05. The study applied the KMO measures of sampling adequacy and Bartlett ’s test of sphericity to test whether the relationship among the variables has been significant or not as shown below in table 4.2. Factor 1 was based on four items

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