CHAPTER 6: QUANTITATIVE RESULTS
6.2 Data Management
The data collection for the quantitative study was undertaken from January 2019 to July 2019. The survey questionnaires were distributed to 253 respondents through a web-based survey and email via random sampling. The respondents were all members of the non-profit organisations based in Gauteng province, whom all participated in the NDA training of improving Governance and non- compliance of NPOs. During the data collection, a survey was sent through to the respondents who had provided their contact numbers email addresses during the training. The researcher sent reminders to respondents after 15 days of posting the survey, and the process was followed by an additional ten days afterwards continuously for six months, then the survey was closed down on the set cut-off date (31 July 2019). Participation in the study was voluntary and emphasised on the first page of the questionnaire; none of the respondents was forced to complete the questionnaire.
The number of answered questionnaires were 209 by the end of July 2019.
The IBM Statistical Package for the Social Sciences (SPSS) version 24 for Windows was used to perform the descriptive statistics and the exploratory factor analysis of the survey sample. Data were entered, and each row and column in SPSS was improved through coding of all the thirty-
132 nine questions. The “Values” column in the SPSS on variable views, were set on a five-point Likert scale as: “1– Strongly Disagree”, “2 – Disagree”, “3 - Neutral”, “4 –Agree” and “5-Strongly Agree”. All the quantitative data were managed using SPSS, and all the numeric responses were entered. Furthermore, IBM SPSS Analysis of Moment Structure (AMOS v.25) was used to perform the structural equation modelling (SEM) to test the hypotheses of the conceptual framework of this study.
6.2.1 Data Screening
To ensure that the data are usable, reliable and valid, SPSS was used to clean the data and screen the data by identifying any outliers in the data or missing data. After the identification of data anomalies, a set of procedures were applied for handling outliers to ensure the accuracy of data analysis. Kurtosis and Skewness were used to assess the normality distribution of the data.
6.2.2 Data Response Rate
The quantitative data was conducted using a survey questionnaire, as indicated earlier in this chapter. This section presents any missing or data anomalies as well as the final response rate for the survey sample.
6.2.3 Missing Data
Any research that involves human beings is crucial for the inspection of any missing data as it is not easy to have data that is complete from every subject (case) of the study (Pallant, 2016).
Missing data is the unavailable values for one or more variables (Hair et al., 2014). The missing of data is common in survey studies (Bryman & Cramer, 2011). Furthermore, missing data can influence the ability of statistical tests to establish relationships in a data set, and as a result, it causes parameter estimates that are biased (Hair et al., 2014). Similarly, Dong and Peng (2013) caution the severe impact that missing data has on quantitative research. These impacts are “biased estimates of parameters, loss of information, decreased statistical power, increased standard errors, and weakened generalizability of findings” (Dong & Peng, 2013, p. 2).
Different views have been put forward on what constitutes an acceptable percentage of missing data in research studies. The proportion of missing data is directly related to the quality of
133 statistical inferences (Dong & Peng, 2013). There is no established cut-off from the literature regarding an acceptable percentage of missing data in a data set for valid statistical inferences.
However, Tabachnick and Fidell (2016) are of the view that if missing data accounts for less than 5% and follows a random pattern, then this should not be seen as a barrier.
According to Enders (2003), the missing data rate of 15% to 20% is prevalent in educational and psychological studies. Schafer (1999), on the other hand, asserted that a missing rate of 5% or less is inconsequential. However, Bennett (2001) maintains that if the missing data is more than 10%, then there is a likelihood that the statistical analysis will be biased. Furthermore, the amount of missing data is not the sole criterion by which a researcher assesses the missing data problem (Dong & Peng, 2013). Tabachnick and Fidell (2012) assert that the missing data mechanisms and the missing data patterns have a more significant impact on the results of the research than the proportions of missing data. Taking into consideration what Tabachnick and Fidell (2012) cautioned, the researcher investigated the patterns of any missing data and not just the amount of missing data. According to Kline (2005), the deletion of cases with missing observations solves the completely missed data to analyse available data.
The number of returned/completed questionnaires was 209, and there were no missing/blank questions. However, nine cases (responses) were discarded as the respondents selected the same answers for all the statements. These responses could not be ignored as they would affect the reliability of the results. The discarding of the nine response resulted in the response rate of above 70% (n=200) to be used for data analysis.
6.2.4 Outliers
Identification of outliers is the second phase in the data cleaning process. The occurrence of outliers can lead to the non-normality of data and distorted statistics (Dong & Peng, 2013;
Tabachnick & Fidell, 2014). Outliers in the data exist due to: incorrect entry of the data, failure to determine a missing indicator in the computer, a case which is not from the intended population or a member that is from the population but has extreme values from a normal distribution (Tabachnick & Fidell, 2014). As mentioned in Chapter five of this study, outliers can either be univariate or multivariate. Since outliers affect the values of the estimated regression coefficients,
134 they can also affect the model fit. Hence it is essential to identify them before analysing the data (Field, 2013).
In this study, Univariate detection method through SPSS was used to identify outliers. The detection of univariate outliers assisted the researcher in identifying cases with minimum and maximum values per variable. The data were first converted to standardised scores (z-scores).
According to Tabachnick and Fidell (2014), the cut-off value for potential outliers is ± 3.29; any value exceeding this number is considered a potential outlier. This study adopted a cut-off value of ± 3.29 for the standardised scores as potential outliers. Field (2009) cautions researchers to pay attention to the potential outliers as they lead to the model fit of the research to be biased. After removing the nine cases (Case 3,5,8, 102, 18,23, 104, 178 and 194) that had the same answer throughout, the outliers were computed, and nine cases with possible outliers were detected. These were cases with z-score of more than ± 3.29. As indicated earlier in this section, the nine cases/respondents selected the same responses on a Likert-scale for all the statements in the questionnaire. The cases were removed from the dataset, leaving the number of responses to 200 from the initial 209, which then translated to a sample size of 79% appropriate for further data analysis. The univariate results for outliers are presented in Table 6.1 below.
Table 6. 1 Univariate Outlier Results
Study Constructs Cases with Standardised value exceeding ± 3.29
Standardised Score (z-score)
Reaction (R) No Cases -2.12424; 2.60107
Learning (L) No Cases -2.22237; 2.79219
Training Objectives (OB) No Cases -1.95293; 1.60706
Training Content (TC) No Cases -2.36565; 2.68556
Behaviour (B) No Cases -1.87719; 1.50513
Results (RES) No Cases -2.15837; 2.04284
Valid N (Listwise)=200
135 After completing the process of identifying and removing the outliers in the dataset, it is crucial to ensure that data are normally distributed before inferring the results from such data. The next section outlines how the normality of variables was analysed.