Instrument Validity

Validity of the scale refers to the extent to which the scale or construct measures what it is intended to measure (Leech, Barrett, & Morgan, 2008). That is, do the items making the construct collectively measure the right thing or they measure something else? Instrument validity can be assessed through three main approaches: content validity/face validity, criterion related validity and construct validity (Cao, 2001). All the three measures were used to capture instrument validity for this research.

4.4.1.1 Content/Face validity

Content validity refers to the extent to which a measure represents a given construct or whether the items adequately capture the essence of the construct domain (Churchill, 1979). It requires the identification of a group of items which are thought to measure the construct (Cao, 2001). However, it is a subjective measure as it generally depends on the opinions based on the literature review and questionnaire pre-tests (Hensley, 1999). For this research, content validity of the scale was achieved through peer review with other researchers and academics within the Lincoln University Faculty of Commerce. The instrument was also reviewed by researchers within the Department of Agricultural Economics at the University of Stellenbosch, South Africa. The scale was also piloted with 21 wineries in New Zealand. The researchers and the wineries were confident that the constructs measure what they intended to measure.

4.4.1.2 Criterion related validity

This is the measure of the relationship between the scale and surrogate measures of the construct (Hensley, 1999). This approach is used to demonstrate the performance of a construct by comparing it with another measure that has been shown to be valid. It requires checking the performance of the construct against some criterion that is known to perform well. Hence, instead of developing new scales, this research opted to adopt scales that have been successfully used in previous studies and modify them where necessary. This approach has been used before. In a review of studies using scale development techniques, Hensley (1999) reports that many of the studies borrowed methods and scales from more developed fields. Further, other empirical studies such as Antia & Frazier (2001) and Tremblay, et al.,

(2003) have used measurement scales from previous research, sometimes with minor modifications to meet their research setting. In particular, this research converted the scales from binary response questions to five point likert scales.

4.4.1.3 Construct Validity

Construct validity attempts to identify or establish the underlying construct being measured and to determine the degree to which the test represents the construct measured (Cooper & Schindler, 2006). This measure is mainly captured through the unidimensionality test (Cao, 2001; Kao, 2007). Unidemensionality refers to the existence of a single construct underlying a set of measures (Gerbing & Anderson, 1988). Unidimensionality is commonly captured through factor analysis, a process of deriving a small number of variables that “hang together” from a fairly large set of items (Leech et al., 2008). The subsets of items that are correlated with one another but largely independent of other subsets of items are combined into factors (George & Mallery, 2009). A factor reflects underlying processes that have created the correlations among variables or items. It therefore represents a good measure of construct validity, which aims at identifying the underlying construct being measured.

There are two common factor analysis methods used for assessing unidimensionality. These are exploratory and confirmatory factor analysis (Hair, Black, Babin, & Anderson, 2010). The former is a technique that helps researchers explore the underlying structure of a collection of items and the latter seeks to test the hypothesis that due to some theoretical support or prior research, certain variables should be grouped together on a factor. With exploratory factor analysis, the researcher takes what the data gives or suggests, while with the confirmatory technique the researcher has pre-conceived view about the actual structure of the data based on what theory or past research suggests. With the confirmatory technique, the analyst assesses the degree to which the data meets the expected structure. As indicated in the preceding subsection, this research adopted, albeit with modifications, constructs from previous studies. However, except for trust which was adopted from Kumar et al., (1995), these studies did not indicate that they performed validity or reliability tests on the scales, which meant that most scales remained largely exploratory in nature. For example, incentives, monitoring, and item criticality were adopted from Fraser (2005) who captured the variables as binary response questions where incentives was captured as (where “1” = if contract has a bonus/penalty payment provision for a given attribute and “0” otherwise), monitoring as (“1” = if winery representatives are involved and “0” otherwise) and item criticality as whether the contracts captured grape quality measurements or not. As mentioned above, Fraser (2005) did not perform validity or reliability tests on these variables.

Items making up the legal framework variable were adopted from the World Bank (2003) but there were no reported validity and reliability results. The results were simply reported in

frequency terms, which suggest that it was unlikely for the study to perform validity and reliability tests which, unlike a simple frequency analysis approach, require determining the relationships between the items making the construct.

Due to the fact that both the World Bank (2003) and Fraser (2005) did not report any validity and reliability results and due to the modifications made to the scales adopted from Fraser (2005), this research felt that the scales were largely exploratory in nature. There were no pre- conceived structure of these variables and hence confirmatory factor analysis was seen as inappropriate for examining the latent structure of the variables used in this research. Instead, the research performed exploratory factor analysis as the objective was to uncover the underlying structure. This approach is in line with Gerbing and Anderson (1988) who suggest that exploratory factor analysis is appropriate in cases where the underlying dimensions of a data set are unknown. Although the trust variable was a prime candidate for confirmatory factor analysis to help assess the degree to which the data meets the expected structure of this variable, the variable has been widely used in empirical research (Friman, Gärling, Millett, Mattsson, & Johnston, 2002; Holdford & White, 1997) and this reassured the current research that the variable is one-dimensional. The variable performed well when subjected to the reliability tests and this further reassured this study that trust measured what it intended to measure.

However, whether to perform or not to perform factor analysis on the data depends on the data being appropriate for the technique. Hair et al., (2010), Stewart (1981) and Coakes, Steed and Price (2008) identify pre-conditions for performing factor analysis on the data as, a sample size of 100 or more, strong and significant correlations (greater than 0.30), linearity, no extreme outliers and a Kaiser-Meyer-Olkin (KMO) Measure of Sample Adequacy of 0.60 or more. The rationale for strong and significant correlation between the items is influenced by the fact that construct validity assumes that factors consist of subsets of items that are correlated with one another but largely independent of other subsets of items. It aims at discovering which set of variables form coherent subsets that are relatively independent of one another (Tabachnick & Fidell, 1990). Factor analysis is essentially concerned with the homogeneity of items (Stewart 1981). Since factor analysis is based on correlations, the importance of linearity cannot be overstated as lack of linearity may degrade the factor solution. Further, factor analysis is sensitive to outliers (Coakes et al., 2008), so outliers have to be identified and then removed from the data set or aligned with the average distribution of the data set.

Once the researcher is satisfied that the data is adequate for factor analysis, the unidimensionality test may be performed. According to Cao (2001) and Pallant (2007) a successful unidimenstionality test through exploratory factor analysis must meet the following criteria:

1. First factor loading should explain more than 40 percent of the variance in the construct. 2. All or most of the items should have higher loadings on the first factor than on subsequent

factors.

3. Three or more items must load on each factor.

This research took account of these pre-conditions. An item correlation matrix was computed and examined to ensure that the correlations were high. The results showed high correlation between the items for all the constructs. The Bartlett‟s Test for Sphericity was used to test for the significance of the correlation matrix and all constructs had items with significant correlations. The research also tested for linearity and the absence of outliers and was satisfied that these conditions were met. These are also the pre-conditions for multiple regression analysis (section 4.4.5 of this chapter) and how they were met is discussed under the multiple regression subsection. Lastly, the KMO measure was used to test the hypothesis that the items do not belong together, and in all cases, the KMO was more than 0.60, which meant that the hypothesis that the items do not belong together was rejected. The research was therefore satisfied that the data for both countries was appropriate for factor analysis. The results are presented in Chapter five.