Data Analysis

The present study consists of a number of related analyses as well as a discussion of the results and their implications. The SPSS statistical package for Windows, version 14.0.0 has been used to analyse both data from the database and data obtained from the survey. A number of statistical analysis techniques were used, as follows.

In Chapter 5, descriptive statistics have been used to describe the features of the primary data (the IJV database). This consists of a frequency table and distribution, cross- tabulation, percentage and cumulative percentage, pie charts, bar charts, and graphs. In addition, primary data analysis of host country location factors has also been conducted in this chapter, with measurement of mean central tendency and standard deviation of dispersion.

In Chapter 6, three different sets of statistical tests have been used. First, the measurement of mean central tendency and standard deviation of dispersion was conducted to measure the relative importance of strategic motives. Next, the analysis of variance (ANOVA) test and equivalent test were used to test the significance of the mean differences of the individual variables in each category. Third, the paired simple t-test was used to test the significant difference of the mean scores of each individual strategic motive between the

foreign parent company and the Thai parent company. This method has been widely adopted by many researchers (for example, Tatoglu and Glaister, 2000).

In addition, many researchers (Tabachnick and Fidell, 2001; Boateng and Glaister, 2003; Easterby-Smith et al., 2002; Chen and Glaister, 2005; Field, 2005) argue that if the sample size exceeds 30, it is reasonable to assume that the sample is from a normal distribution and parametric tests can be employed. However, both parametric tests (either the two- sample t-test or ANOVA test) and equivalent non-parametric tests such as the Mann- Whitney U test and Kruskal-Wallis H test were also conducted in this study to remove any doubts which might arise from the nature of the data.

The preliminary data analysis indicated that there was overlapping or relatedness of the variables in each category in Chapters 6 and 7. Hence, the exploratory factor analysis (EFA) technique of the Anderson-Rubin method was used to create a parsimonious set of distinct non-related variables. A number of researchers (Tabachnick and Fidell, 2001; Field, 2005) argue that this method is a suitable option when uncorrelated scores are required. After the EFA technique had derived the underlying factors from the set of variables, significant mean score difference tests – ANOVA and equivalent test – were conducted with those underlying factors in Chapter 6, while a multiple regression test was run with those underlying factors in Chapter 7.

In Chapter 7, in addition to the measurement of the mean central tendency and standard deviation of dispersion, the correlation coefficients were computed to measure the correlations of the variables. Further, multiple regression was conducted to ascertain the multivariate relationship between the independent variables and the dependent variables, since numerous researchers state that the true relationship of the variables must be proved with both the bivariate relationship test and the multivariate relationship test (for example, Sim and Ali, 1998; Demirbag and Mirza, 2000; Boateng and Glaister, 2002).

Moreover, the data were inspected for multi-collinearity and autocorrelation before the regression analysis was conducted. Though autocorrelation is likely to occur with time series data, it might occur with cross-sectional data as well. This problem results from the

fact that errors are not independent of each other. In other words, the errors have correlated among themselves. The analysis of Durbin-Watson statistics was then used to detect this problem. The test statistics can vary between 0 and 4. A value near 2 (1.5-2.5) means the residuals are uncorrelated. If the value is greater than 2.5, it indicates a negative correlation between the adjacent residuals. A value below 1.5 is interpreted as a positive correlation. For this study, the evidence showed that no serious autocorrelation had emerged. The value of the Durbin-Watson can be seen in the result analysis section in each chapter.

Multicollinearity occurs when two or more independent variables are linearly related very closely. This problem was also monitored. Muthen and Lehman (1985) argue that a correlation with a value above 0.70 should be considered a serious problem. After the simple correlations between independent variables and standard errors of the estimated coefficients had been inspected, the data showed that there was no serious multicollinearity which would distort the efficiency of the estimation.

Three different sets of statistical analysis techniques were used in Chapter 8. First, the correlation coefficients were computed. Then, the related variables were further tested with multiple regressions in order to study the relationship between the independent variables (the related factors) and the dependent variable (the overall IJV performance). Following the practice of previous studies (Sim and Ali, 1998; Boateng and Glaister, 2002), the dependent variable of this study, overall IJV performance, was measured using a composite index (an arithmetic average score). Respondents quantified their satisfaction with the IJVs in respect of five activities on a 5-point scale where ‘1’ denoted ‘much worse than expectation’ and ‘5’ denoted ‘much better than expectation’). The five activities were marketing, finance, strategy, technology transfer and R&D, and human resource management.

In addition, the t-test and equivalent test were conducted to test the significant difference of the mean scores of some variables in this chapter.