Lugares que visitan o que quisieran visitar

In order to answer the questions raised from the literature review and drawing a meaningful conclusion, a large sample of all groups is required at first, as the small sample will not lead to appropriate analysis as well as the inability of generalization. Determining the suitable statistical tests to be used is crucial matter as these tests underpin the results that eventually lead to actual accurate conclusion. These tests however, rely mainly on the objectives of study. Thus, these tests are linked to each objective independently as presented in table (3.16).

Table 3.16: Statistical tests and objectives of study.

Statistical test Reasons for using the

particular test.

Objective

Descriptive analysis

To describe the trend of results using simple statistical model such as

mean (Field, 2009).

For all objectives

Two-Sample T- Test (independent

sample t-test).

To compare the mean of two groups from sample with

each other (Field, 2009, Wooldridge, 2005).

For all objectives to compare the perceptions of financial managers and

145

Anova To examine differences

among the means for several various groups as whole (more than three, as

independent t-test is used for two groups) (Field,

2009).

For objective three to compare the perceptions

of financial managers across different economic sectors, legal forms, sizes,

and management structures. Person

correlation’s coefficient

To express the relationships between variables (Field,

2009).

For objective five to make a correlation between problems and expected contributions result from using IFRS for SMEs, in order to identify the role of

IFRS for SMEs in mitigating or solving

problems.

After performing Cronbach Alpha for reliability and factor analysis for validity, choosing these tests was according to the nature of both outcome variables (dependent) and the predictor variable (independent) as well as the number of each one of them (Field, 2009). As outcome variable was continuous for all objectives and the predictor variable was categorical, independent sample t-test to make a comparison between two groups and one-way Anova test to compare the perception among more than two groups are specified to be the most appropriate tests to achieve the objectives in addition to some descriptive analysis by using mean (Field, 2009). The Parson correlation’s coefficient is conducted to express the relationship between two continuous variables that are between dimension three and eight. Post-hoc comparisons within Anova are conducted to specify the place that make the differences among groups (Field, 2009).

Because factor analysis has been performed, all these tests were executed based on factors determined, whereas each factor comprises a number of questions within each dimension as explained in the next section. Analyzing data based factors facilitate the analysis processes and make the data more digestible instead of

146

investigating each question in the questionnaire separately (Field, 2009; Pallant, 2011).

Conducting these tests can only be performed if the assumption of normality of distribution and homogeneity of variance are met. For this purpose, the researcher took action to ensure these assumptions as explained below.

3.15.1 Normality of distribution.

Due to using a large sample, ensuring the normality by relying upon significant tests of Skew and Kurtosis is deemed ineffective “because they are likely to be significant even when skew and kurtosis are not too different from normal” (Field, 2009:138). Similarly, The Shapiro–Wilk test in large samples “can be significant even when the scores are only slightly different from a normal distribution.” (Field, 2009:148). Therefore, the central limit theorem regarding the normality of distribution has been applied that “demonstrated that as samples get large (usually defined as greater than 30), the sampling distribution has a normal distribution with a mean equal to the population mean” (Field, 2009:42).

3.15.2 Homogeneity of variance.

It is referred to “that the variances of one variable should be stable at all level of other variables” (Field, 2009:149). The homogeneity of variance can be tested by using Leven’s test whereas the significant result means that the homogeneity of variance has been violated and vice versa for non-significant result from Leven’s test (Field, 2009:342, Pallant, 2011). This test has been conducted in this study for each factor determined by factor analysis within each dimension.

The results of Leven’s test for each factor are presented in the analysis chapter. With this respect, if the result of significance of Leven’s test is more than .05, which means that homogeneity of variance assumption is met. In this case the listed tests in table (3.16) above can be executed to the pertinent factor. However, if the result of this test is significant, the researcher reports the result in different way within the same test as illustrated in table (3.17).

147

Table 3.17: Alternative reports according to the homogeneity of variance results.

Test If the homogeneity

of variance is met.

If the homogeneity of

variance is doubtful. Reference

Two-Sample T- Test (independent sample t-test).

Report the result “at the row in the table

labelled Equal variances assumed”, which is

the first row in the table.

Report the result “at the row in the table

labelled Equal variances not assumed”, which is the second row in the

table.

(Field, 2009:342, Pallant, 2011).

One-way Anova

Report the result in the “table labelled

ANOVA”.

Report the result in the “table labelled

Robust Tests of Equality of Means”, through wetch Anova.

(Field, 2009:388, Pallant, 2011). Procedures for Post-hoc comparisons within Anova Tukey’s HSD and Hochberg’s GT2 procedures. As the size of groups could

be either similarly equalized or differed. Games–Howell procedure, as the homogeneity of variance is doubtful. (Field, 2009).

Correlation Parson correlation’s

coefficient. Spearman’s correlation coefficient. (Field, 2009, Pallant, 2011).

Using the aforementioned reporting within the parametric test instead of applying non-parametric test such as the Chi-Square Test, Wilcoxon rank-sum test and/or Mann-Whitney test, and the Kruskal-Wallis test, can be justified as follow:

1. “In large samples Levene’s test can be significant even when group variances are not very different” (Field, 2009:152). Thus, the violation of homogeneity of variance is doubtful.

2. As the researcher performed Levene’s test for each factor within each dimension independently in order to increase the accuracy of result instead of taking the overall variances in one sum, reporting the results from the same test by using alternative reporting provided in the same test for different

148

variances assumption would increase the consistency and constancy of interpreting the results. Especially the majority of factors across dimensions satisfied the homogeneity of variance assumption.

3. Using parametric tests is deemed more robust than use of non-parametric test.