4.15 Data analysis
4.15.6 Confirmatory Factor Analysis (CFA)
Albright and Park (2009) claim that CFA is a theory or hypothesis driven analysis and enables researchers to test hypotheses. In addition, CFA produces many goodness-of-fit measures that could be used to assess a research model. CFA is considered to be a special case of SEM (Albright & Park, 2009). While SEM has been found to be a combination of a measurement model and a structural model, CFA represents the measurement model of SEM. The main advantages of using CFA are (Mueller & Hancock, 2008):
It bridges the commonly seen gap between theory and observation.
It gives valuable information to the researcher regarding the fit of the data to the theory driven model.
It can point out potential weakness of specific items in the model.
It acts as a process comprising model conceptualization, identification, parameter estimation, data-model fit assessment and potential re-specification of the model.
It can enable researchers to reject models or theories.
Limitations of CFA include the lack of strictly confirmatory characteristic in the analysis during post hoc modifications as during such a modification the model could turn out to
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be somewhat exploratory (Conway & Huffcutt, 2003) Similarly small samples could be cause of concern as assumptions due to violations in normality could be invalid and hence the model may not fully fit to the data (Raykov, 1998). Similarly usually the number of participants is usually lower than the number of degrees of freedom which is not accepted when Maximum Likelihood estimation method is used (McCrae et al. 1996). Therefore the researcher while using CFA in this research ensured that the limitations are adequately taken care of. In addition the researcher could find support from other researchers (e.g. Cuskelly et al. 2006) involved in volunteer research who have successfully used CFA.
While the foregoing discussions have dealt with the data analysis steps used in this research, there are two other tests that need to be discussed namely unidimensionality and common method bias. While unidimensionality indicates the presence of only one underlying dimension in the model it also suggests that the reliability values are acceptable as reliability assumes unidimensionality (Gerbing & Anderson, 1988). Similarly common method bias occurs when variables both independent and dependent are given by the same source (e.g. by the same individual) at any instant of time (Serenko, 2008) and also due to self-reporting (Meade et al. 2007). This is indicated by the emergence of one single general factor when the data are subjected to analysis (Serenko, 2008). Presence of method bias can raise questions on the validity of the conclusions as it makes it difficult to make out whether the relationships in the model represent reality or just the feelings of the participant (Podsakoff et al. 2003). While assessment of unidimensionality was conducted using AMOS output on the regression estimates and critical ratio (CR) generated using the maximum likelihood (ML) method, method bias was assessed using average variance extracted (AVE) method, use of these methods is supported by extant literature. For instance (Janssens et al. 2008) argue that unidimensionality could be tested using the AMOS output on regression weights (generated using ML method) while (Merrilees et al. 2011) suggest that AVE could be used to determine the presence of common method bias. The actual measurements with respect to unidimensionality and common method bias are provided in Chapter 5.
129 4.16 Summary
In this chapter the discussions have focused on the methodological aspects adopted in this research based on a critical discussion on various aspects that influence the adoption of a particular methodology. The discussions enabled the researcher to develop the research framework which included the adoption of positivist epistemology, objectivist ontology, deductive research approach and quantitative research method for this research. Further the chapter has provided the details on the research design and research strategy developed and implemented for this research. The data collection and data analysis aspects have been discussed comprehensively. Thus this chapter provides the basis for analyzing the data details of which are provided in the next chapter.
Chapter 5
130 5 Introduction
This chapter makes available the complete details related to the data analysis of the data collected using the quantitative methodology described in the previous chapter. The results of the analysed data are reviewed and interpreted to arrive at inferences. The various steps involved the data analysis and interpretation include analyzing descriptive statistics, testing the internal consistency reliability of the collected data, testing the construct validity, Structural Equation Modelling comprising the Confirmatory Factor Analysis (CFA) and path analysis, testing the unidimensionality of the solution, establish the relationship between latent variables, provide discussions on the findings of the statistical analysis and test the hypotheses. SPSS v. 17 and AMOS v.18 were used in this research to conduct the statistical analysis. Each one of the above statistical tests is described next in the following sections.
5.1 Descriptive statistics
Descriptive statistics include both demographic variables and the variables concerning the research model. The demographic descriptive statistics are analysed first prior to analyzing the variables in the model. The data were analysed using SPSS version 17.0.
5.1.1 Demographic variables
Data on five demographic variables namely gender, age, educational qualification, income and number of years of service as volunteer were analysed. Following are the details. As far as gender is concerned majority of the respondents who participated in the survey was female (63%) although the male participants’ percentage (37) was slightly higher than half the percentage of the female participants. This indicates that volunteers from both the genders participate as volunteers indicating that gender is not a factor that affects volunteerism. Figure 5.1 provides the graphical representation of the age of the respondents who participated in the survey. The chart indicates respondents in the age group 51-60 years were the maximum (28.5%) who participated in the survey followed by 19.9% in the age group 41-50 years, 17.1% in the age group 61-70 years, 15.3% in the age group 31-40 years and 14.2% in the age group 21-30 years. In comparison, the percentages of participants in the age group of 18-20 years and beyond 70 years of age
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were insignificant. This shows that the maximum number of respondents were in the age group between 21-70 years indicating that age does not act as a barrier to be a volunteer. Age as a factor does not indicate that one particular age group to be dominant as volunteers due to any special reason from which it is possible to infer that age is not a variable in this research.
Figure 5.1 Age of the participants in the survey
Similarly, in case of educational qualification Figure 5.2 indicates that the educational qualifications do not significantly contribute any particular information regarding the participants except that volunteers have different levels of qualifications. For instance 27.5% of respondents were holding a bachelor’s degree while 28% where having just secondary school educational qualification. Interestingly only 16.6% of the respondents were having postgraduate qualifications while intermediate school and diploma holders accounted for 13.7% and 13% respectively. These figures indicate that educational qualification is not a significant criterion to become a volunteer and that people with different qualifications could become volunteers.
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Figure 5.2 Level of education of participants in the survey
With regard to annual income Figure 5.3 indicates that volunteers in different income brackets have participated in the survey. Income of participants has ranged from less than $1000 per annum to beyond $100,000 per annum indicating that income is not a factor that prohibits or encourages people become volunteers.
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Lastly participants were asked about the number of years they have been volunteers. Figure 5.4 provides the details.
Figure 5.4 Number of years participants have been volunteers
Descriptive statistics was used in the research model to analysis the data as the first step. The results of the collected data were analysed using median (Table 5.1). The mean of the data collected ranged from 3.29 (Management Practice) to 4.22 (Retention (One)). As far as volunteer management is concerned the central tendency indicated that participants agreed (median 3.375) that volunteering organisations often used particular management practices in dealing with the volunteers. The underlying meaning that could be extracted from the responses, appears to indicate that volunteering organisations do not use management practice as standard and routine process always but use it most of the time. The participants appear to indicate that volunteering organisations tend towards using management practices, a sign that could indicate that volunteer organisations need to always use management practices not most of the time.
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No Model Constructs Description Mean Median Std. Deviation
1 MGMNT Management Practice 3.2940 3.3750 0.88790
2 SAT Satisfaction 4.0486 4.0000 0.61370
3 MOT Motivation 4.1634 4.1818 0.56419
4 RET Retention 4.0026 4.0000 .78422
Table 5.1 Descriptive statistics
In similar vein it can be seen from Table 5.1 that the central tendency indicates that participants are satisfied (median 4.0) with their organisations indicating that the organisations are managing them well and the management practices has led to volunteer satisfaction. As far as volunteer motivation is concerned the central tendency appears to indicate that participants are motivated by their organisations (median 4.1818) indicates that volunteering organisations have management practices that motivate the volunteers. Finally, participants in the research indicated that they would like to continue with their respective organisation and would not like to leave the organisation in the immediate future (median 4.0) indicating that overall the volunteers are probably happy with the way their organisation is managed and they are perhaps motivated and satisfied with the management practice.
As far as the standard deviation is concerned all constructs in Table 5.1 are seen to have a deviation within a maximum of 0.888 (for Management Practice). It is seen that the values of the central tendency parameters mean and standard deviation derived from the descriptive statistics are similar to those achieved by earlier researchers, for instance Cuskelly et al. (2006) who achieved a mean of 2.8 for recruitment (a management practice construct) and 3.94 for recognition (a management practice construct).
As part of the descriptives the next step involved cleaning the data and preparing the data for data analysis. As discussed in Section 4.14.6 normality (skewness, kurtosis and Mahalanobis distance) and multicollinearity were checked (see Appendix Normality- Appendix 6) and were found to be within acceptable limits. For instance data was checked for normality. Values fixed as limits were skewness ±1.5, kurtosis ±3.0 and Mahalanobis distance less than 4.0. Although some responses showed values of
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Mahalanobis distance exceeding 4.0 (Appendix 6) such responses were much less when compared to the total number of responses (386), thus making the data distribution to be considered as normal.
Similarly, multicollinearity was checked using correlation between any items under a construct and the limit fixed was less than 0.9. These limits were fixed based on prior research (e.g. Pallant, 2005). Further to the analysis on descriptive statistics, the researcher proceeded to test the reliability and validity of the data beginning with assessing the internal consistency of the collected data. As explained in Section 4.12, internal consistency is checked using reliability (Cronbach’s Alpha) test, item-item correlation and item-total correlation.
5.2 Reliability
The reliability test was carried out on the constructs identified for this research the list of which is provided in Table 5.2. The table provides the figures for Cronbach’s alpha, inter-item correlation and item-item correlation, derived from SPSS version 18.
Measurement Items (Interval Scale) Items Cronbach’s Alpha Reliability Results Inter-Item Correlation (range) Item-Total Correlation (range) Management Practice (MGMNT) 24 0.961 Good 0.225 (MP14- MP20)-0.753 (MP10-MP11) 0.561-0.778
Satisfaction (SAT) 9 0.909 Good 0.389 (SAT25- 30)-0.789 (SAT32-33)
0.619-0.743
Motivation (MOT) 11 0.909 Good 0.235 (MOT
36-42)-0.776 (43-44)
0.563-0.749
Retention (RET) 6 0.827 Good 0.24 (RET47-
49) – 0.8 (RET48-50)
0.503-0.707
Table 5.2 Internal consistency readings
As mentioned in Section 4.12 acceptable values of Cronbach’s alpha should exceed 0.7 for data to be considered reliable. It can be seen from Table 5.2 that Cronbach’s alpha for all the constructs exceed 0.7 with minimum being 0.827 and maximum being 0.961
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indicating that the data are reliable. With regard to internal consistency measurement it can be seen that item-item correlation values in some cases were found to be lower than the reference value of 0.3 set for this research (see Section 4.12). For example in the case of the construct Management Practice, five correlation values were found to be lower [MP14-MP20 (0.26), MP9-MP14 (0.245), MP1-MP14 (0.289), MP8-MP20 (0.26) and MP13-MP20 (0.225)]. However all these items which contributed to lower values were still retained because the contents of these items were important and could not be ignored until they have been subjected to more rigourous statistical tests. Similar arguments could be provided for retaining items that had lower item-item correlation with regard to the constructs Motivation and Retention. Thus while retaining the items even with lower than acceptable values of inter-item correlation, the researcher kept the option to retain or delete the items based on further tests. In this context it could be seen that the item to total correlation readings for all the constructs indicated values above the reference value of 0.5. Thus it can be said that internal consistency of the items were achieved.
5.3 Validity
As explained in Section 4.13 content validity, construct validity and discriminant validity were tested in this research. Content validity was tested based on the outcome of the pilot tests and as explained in Section 4.13. Experts were approached to examine the validity of the contents in terms of the language, format, scales used and the ability of the contents to measure the constructs they are purported to measure. The final set of questions validated by experts and used in the main survey is given in Table 5.3.
Question number
Description Value Measure
Management practice
MP1 Identify potential volunteers before events begin. 5-points Ordinal MP2 Provide role or job description for individual volunteers. 5-points Ordinal MP3 Actively encourage turnover of volunteers in key position. 5-points Ordinal MP4 Maintain database of volunteers’ skills, qualification and experience. 5-points Ordinal MP5 Match the skills, experience and interests of volunteers to specific roles. 5-points Ordinal MP6 Develop positions to meet the needs of individual volunteers. 5-points Ordinal MP7 Actively recruit volunteers from diverse background. 5-points Ordinal MP8 Use advertising for volunteer recruitments (e.g., newsletters, online,….) 5-points Ordinal MP9 Encourage volunteers to operate within a code of acceptable behavior. 5-points Ordinal MP10 Introduce new volunteers to people with whom they will work during the 5-points Ordinal
137 organisation.
MP11 Provide support to volunteers in their roles (e.g., assist with the resolution of conflict).
5-points Ordinal
MP12 Manage the work loads of individual volunteers where they are excessive. 5-points Ordinal MP13 Assist volunteers to access training outside the organisation (e.g.,
accreditation training course)
5-points Ordinal
MP14 Cover or reimburse the costs of volunteer attendance at training or accreditation course.
5-points Ordinal
MP15 Conduct induction sessions for specific group of volunteers (e.g. supervisor,
team leader, ….) 5-points Ordinal
MP16 Mentor volunteers, particularly when staring in a new role. 5-points Ordinal MP17 Provide sufficient support for volunteers to effectively carry out their task. 5-points Ordinal MP18 Recognize outstanding work or task performances of individual volunteers. 5-points Ordinal
MP19 Plan for the recognition of volunteers. 5-points Ordinal
MP20 Thank volunteers for their efforts (e.g., informal thanks you) 5-points Ordinal MP21 Publicly recognize the efforts of volunteers (e.g. in newsletters, special
events).
5-points Ordinal
MP22 Provide special awards for long serving volunteers (e.g., life membership). 5-points Ordinal MP23 Monitor the performance of individual volunteers. 5-points Ordinal MP24 Provide feedback to individual volunteers. 5-points Ordinal
Satisfaction
SAT1 My relationship with paid staff. 5-points Ordinal
SAT2 How often the organisation acknowledges the work I do. 5-points Ordinal SAT3 The degree of cohesiveness I experience within the organisation 5-points Ordinal SAT4 The chance I have to utilize my knowledge and skills in my volunteer
Work.
5-points Ordinal
SAT5 The access I have to information concerning the organisation. 5-points Ordinal SAT6 The freedom I have in deciding how to carry out my volunteer assignment 5-points Ordinal SAT7 My relationship with other volunteers in the organisation 5-points Ordinal SAT8 The amount of interaction I have with other volunteers in the organisation 5-points Ordinal SAT9 The amount of time spent with other volunteers. 5-points Ordinal
Motivation
MOT1 No matter how bad I've been feeling, volunteering helps me to forget about it.
5-points Ordinal
MOT2 I am concerned about those less fortunate than myself. 5-points Ordinal MOT3 I am genuinely concerned about the particular group I am serving. 5-points Ordinal MOT4 I feel compassion toward people in need. 5-points Ordinal
MOT5 I feel it is important to help others. 5-points Ordinal
MOT6 I can do something for a cause that is important to me. 5-points Ordinal MOT7 Volunteering allows me to gain a new perspective on things. 5-points Ordinal MOT8 Volunteering lets me learn things through direct, hands on experience. 5-points Ordinal
MOT9 I can explore my own strengths. 5-points Ordinal
MOT10 Volunteering increases my self-esteem. 5-points Ordinal
MOT11 Volunteering makes me feel needed. 5-points Ordinal
Retention
RET1 I plan to continue volunteering at this organisation until end of this year. 5-points Ordinal RET2 I plan to continue volunteering at this organisation next year 5-points Ordinal RET3 I am likely to be volunteering at this organisation three years from now. 5-points Ordinal
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RET4 I intend leaving this organisation altogether within 12 months 5-points Ordinal RET5 I intend to volunteer in the next 12 months but with a different
organisation
5-points Ordinal
RET6 I intend to cease volunteering at organisation as soon as another volunteer can be found to replace me
5-points Ordinal
Table 5.3 Description of items, their coding, scaling and the constructs they measure
As far as construct validity was concerned, as explained in Section 4.13, it was measured using convergent validity. Convergent validity was measured using internal consistency measurement using the inter-item and item-total correlations. From Table 5.2 it can be seen that internal consistency measures with regard to item-total correlations are in line with the reference value of >0.5 while majority of the item-item correlations are >0.3. While some values of item-item correlation were below 0.3 (see Table 5.2), those items causing concern were retained to test the validity further before any decision could be taken to delete them. Thus considering the fact the item-total correlation values exceed the reference value of 0.5, even though some inter-item correlations were lower, the convergent validity was considered to have been achieved. Further to testing the construct validity, a detailed discussion on the discriminant validity was considered necessary although the discussion on this is provided under later Section 5.7.2 as part of the confirmatory factor analysis.
At this point it is important to understand that the data analysis conducted up to this point is common to both the models provided in Figures 3.2 and 3.3. Beyond this point it was necessary to analyse the data with regard to the two models will be different. Thus the model provided in Figure 3.2 was tested first. The tests involved CFA and SEM. Outcome of the analysis is provided in Appendix 7. From Appendix 7 it can be seen that none of the five constructs namely planning, recruitment, training and support, recognition and performance management are significantly related to retention. Similarly none of the five constructs were significantly related to either motivation or satisfaction