This section provides an insight into the measures that were used in the data analysis. First, descriptive statics were conducted to present a quantitative description of findings and determine some emerging patterns in the data. Frequencies cross-tabulations and Cramér’s V tests were conducted. Following this, the model was applied to the data and further analysis was conducted to determine any key associations between the variables. One-Way ANOVA and binary logistic regression was conducted to determine the
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impact of SHRM approach and union presence on EV mechanisms in foreign-owned MNEs operating in Australia.
4.4.1 Descriptive Statistics
The descriptive analysis was conducted and the raw data was transformed into a form that would present information to describe a set of factors in a situation. Descriptive statistics are used to illustrate and analyse the demographic characteristics of the respondents in the form of frequencies and percentage. The main aim is to present quantitative description in a simplistic form that is straightforward and easy to comprehend.
4.4.2 Frequencies
Frequency distribution is used to condense and summarise large amounts of data in a useful and easy to understand format. Raw numbers are converted into percentages and provide a useful description of the data. Frequency distribution will be used as a means to make early inferences about the character of the population from the sample of 549 MNEs in Australia. Frequencies facilitate graphical representation of the data so the data can be displayed in a manner that is easy to comprehend. Through the frequencies basic statics such as mean, mode, and standard deviation are used to describe the data set.
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4.4.3 Cross-tabulations
Cross-tabulation aids the researcher in organising that data collected by group categories and classes to fit comparisons. It is a joint frequency distribution of observation on two or more variables. Cross-tabulations are an extremely useful and analytical tool designed to analyse categorical (nominal measurement scale) data (Barghoorn, 1996). This study utilises cross-tabulations to record the frequency of responses and also to make inferences about the different predictor variables: trade union recognition, SHRM approach, American country of origin, establishment size, whether the company established as a greenfield site, industry or sector, date of establishment and a SHRM orientation including HR department size, shared HR services, presence of HRIS and Australian HR representation. It also uses cross-tabs to determine the minimalist and dualist dimension of EV. The cross-tabs indicate whether the MNE uses both direct and indirect dimensions of EV. They also help to differentiate between MNEs that have no direct or indirect elements of EV. Furthermore, the cross- tabs help to determine the relationship between the dimensions of EV and the predictor variables.
4.4.4 Cramér’s V Tests
The Cramér’s V test is used further to the Chi square tests to determine correlation between variables. The Cramér’s V test is a post-test and aids in analysing the strength of the association after the Chi-square has determined the significance between two
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variables (Cramér, 1999). To determine the V, it is calculated by first calculating the Chi-square and then using the following equation.
V = SQRT( c2 / (n (k - 1)) )
where c2 is chi-square and k is the number of rows or columns in the table (Cramér, 1999).
In this thesis, the Cramér’s V test investigates proportion differences between the use of EV practices in the four EV archetypes (direct, indirect, minimalist and dualist). This test enables categorisation of MNE practices in Australia according to the four archetypes of EV.
4.4.5 One Way ANOVAs
The one-way analysis of variance is a method used for analysing the difference between two or more sampled relationships. It is a test that uses only one independent variable to compare mean differences in two or more groups. It assumes that the means are normally distributed and can be achieved by subdividing the total sum of squares. This thesis uses the one-way ANOVA test to analyse the differences between the four EV archetypes: minimalist, dualist, direct and indirect.
4.4.6 Binary Logistic Regression
Binary logistic regression analysis is a useful econometric tool that can be used to predict the presence or absence of a characteristic or outcome based on values of a set of
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predictor variables (Hartman, 2000). Binary logistic regression was used to make inferences about the relationships between the EV archetypes and the predictor variables. It was run separately for each of the EV archetypes: direct, indirect, minimalist and dualist. Binary logistic regression is generally suitable to use when the dependent variable is dichotomous. The logistic regression coefficients can be used to estimate odds ratios for each of the independent variables in the model. The binomial logistic regression calculates b-weights or regression weights. However, these b-weights are not utilised to predict scores but they are instead applied to the logit, that is, the natural logarithm of the odds ratio. The odds ratio is somewhat like a probability and is the ratio of the numbers in one category to the number of cases in the other category. The logistic regression equation has the form:
The rationale for using binary logistic regression is to explore the four EV archetypes individually and understand their associations with the predictor variables.