Statistical Techniques

According to Zikmund (2003) there are three major criteria that can be used to evaluate measures: (1) reliability, (2) validity and (3) sensitivity.

5.7.1.1 Reliability

As mentioned, reliability refers to the assessment of the degree of consistency between multiple measurements of a given variable (Hair et al., 2005). A measure is said to be reliable when it provides consistent results under similar circumstances (Churchill, 1979). According to Churchill (1979) coefficient alpha is the appropriate means of ensuring the reliability of a measure and should be the first measure used to calculate the quality of an instrument. The results of the reliability test for the data are discussed in chapter six.

5.7.1.2 Validity

To ensure that the research instrument measured what it was intended to measure, the validity of the research instrument was assessed. According to Zikmund (2003) there are three basic approaches that can be used to evaluate the validity of an instrument: (1) content validity, (2) criterion validity and (3) construct validity.

Content validity was ensured by having two academic professionals review the items and scales before administering the questionnaire. Face validity is the extent to which a measure is subjectively viewed as covering the concept it purports to measure (Zikmund, 2003).

Criterion validity assesses the extent to which a measurement scale performs as expected, compared with other variables selected as meaningful criteria (criterion variables). The criterion variables may include attitudinal and behavioural measures, or scores taken from other scales (Malhotra, 2004). The study makes use of a questionnaire, formulated from a literature review, which denotes relationships and associations among the different constructs investigated. If relationships in a hypothesised model could be confirmed, it would be considered as evidence of the existence of content and criterion validity.

Construct validity refers to the validity of inferences that observations or measurement tools actually represent or measure the construct being investigated (Churchill, 1983). As the theory of planned behaviour as well as the scales were designed for Western culture respondents, but were used in the East African market, it was necessary to test for construct validity to ensure that the scales and dimensions had been interpreted by respondents as intended. When construct validity is assessed, the researcher tries to answer theoretical questions about why a specific scale works and what inferences can be made in terms of the underlying theory (Malhotra, 2004). When assessing construct validity it is important for the researcher to have established the meaningfulness of the measure by means of discriminant and convergent validity (Zikmund, 2003).

Discriminant validity represents how unique or distinct a measure is (Zikmund, 2003). Discriminant validity ensures that concepts or measurements that are supposed to be unrelated are, in fact, distinct. To test for discriminant validity the researcher conducted an exploratory factor analysis that included all items that measured the independent variables. An exploratory factor analysis is employed to condense a number of variables to a manageable number that belong together and display overlapping measurement characteristics (Cooper and Schindler, 2003). The results of the exploratory factor analysis are discussed in chapter five. Thus, if a scale shows discriminant validity when conducting an exploratory factor analysis, it can also be said to show convergent validity.

5.7.1.3 Inferential Statistics

Inferential statistics were used as a basis for the rejection or non-rejection of the eight hypotheses. The hypotheses referred to relationships between the independent variables and intention to open a formal bank account, the dependent variable. To test whether significant relationships exist between the dependent variable and independent variables it is necessary to conduct regression analyses.

i. Linear Regression Analysis

A regression analysis is a statistical tool for the investigation of relationships between variables (Sykes, 2010). Regression analyses were used to test the relationships proposed in the study’s hypotheses. Regression analysis is appropriate for the study as it is used when there are multiple independent variables and a single dependent variable (Sykes, 2010). When conducting a regression analysis, it is necessary to ensure that multicollinearity is not present (Zikmund et al., 2010).

Multicollinearity occurs when two or more predictor variables in a multiple regression model are highly correlated (Zikmund et al., 2010). Mulitcollinearity was avoided in this study by ensuring construct validity and that all VIF values were below ten (Zikmund et al., 2010).

When analysing the results from the regression analysis it will be important to study the following outputs: (1) the significance value, (2) the F value, (3) the sum of squares values, (4) the R-square value, (5) the VIF and the IV (1-R²) (Gupta, 2000).

The significance value determines the goodness of fit or the degree to which the model explains the variation in the dependent variable. If the significance value is below 0.05 it can be concluded that a fit exists between the model and the data (Gupta, 2000). The F value statistic tests the overall significance of the regression model. The F value tests the null hypotheses by determining if all the regression coefficients are equal to zero (Gupta, 2000). The sum of squares values are comprised of the: (1) total sum of squares, (2) the explained sum of squares and (3) the residual sum of squares (Gupta, 2000). The total sum of squares describes the total variation in the dependent variable (Gupta, 2000). The explained sum of squares is the portion of total sum of squares that can be accounted for by the independent variable and the residual sum of squares is that which cannot be accounted for by the independent variable. The R-square measures the proportion of

the variation in the dependent variable that is explained by variations in the independent variables (Gupta, 2000). Lastly, the VIF as well as tolerance values were studied to ensure that they were below ten and above 0.1 respectively as this is indicative of the absence of multicollinearity (Gupta, 2000).

5.8 CONCLUSION

As one can see, the methodology provided a constructive generic framework, which was followed in detail by the researcher. This framework began with secondary research in the form of a literature review in order to develop a theoretical foundation upon which to commence the study of the effect of attitudes and intentions on the adoption of financial services in Tanzania. Secondary research was conducted before primary research in the study due to practical reasons and to prevent the study from producing redundant research.

Primary research was then addressed in the framework and was implemented in the form of both qualitative and quantitative research. Qualitative research allowed for exploratory research to be conducted in order to increase the understanding of the concept, to clarify the exact nature of the problem to be solved and to identify important variables to be studied (Zikmund et al., 2010). Quantitative research consisted of a cross sectional survey design that allowed for the collection of raw data by means of a questionnaire at the Dar es Salaam Librarian Training School.

Before the questionnaire was administered in Tanzania it was tested in a pilot study in South Africa to ensure reliability of the scales used and success of the research design. The raw data collected in Tanzania was then analysed using inferential statistics (regression analysis) to test the relationships between the independent and dependent variables proposed in the study’s hypotheses.

As can be seen, the methodology was meticulously planned and implemented by the researcher. It was necessary for such precision to be taken, as the data obtained from the methodology would determine the quality of the results and recommendations suggested by the study.