CHAPTER 3 METHODOLOGY AND RESEARCH DESIGN
3.7 Techniques of the Quantitative Data Analysis
which is unquestionably less than .95. The highest variance inflation factor (VIF) statistic was 3.867 (table 4.7), which was also less than 9.5. Hence, there was no chance of a multicollinearity problem among or between the variables.
3.6.7 Test of Autocorrelation
The multiple regression analysis leaves no room for correlation by chance or autocorrelation, which means that the value assumed can never be equivalent to any prediction. The Durbin Watson statistics tests the presence of autocorrelation. Its value between 1 to 3 is normally accepted as proof of auto correlation.
The autocorrelation was also tested in this study with the use of Durbin- Watson statistics, which revealed consecutively 1.939, 1.792, 2.195 and 1.628, which certified undoubtedly that there was no autocorrelation (Appendix D).
standard deviation, and percentile were also analyzed for ensuring the level of agreement for all items.
3.7.3 Bivariate Analysis
Usually when a researcher wants to see the relationship between two variables, bivariate analysis is needed. Bivariate analysis helps to describe the correlation and association among the variables. The Pearson correlation analysis was used for the bivariate descriptive analysis. It helped to show the correlation and association between two variables.
3.7.4 Multivariate Analysis
Hair et al. (2010) stated that “multivariate analysis refers to all statistical techniques that simultaneously analyze multiple measurements on individuals or objects under investigation” (Hair et al., 2010). The key research question of the current study is “how the set of independent variables transformational leadership and perceived organizational support influence the dependent variable of public service innovation outcomes?” The study also predicted to answer the role of the moderator of perceived organizational culture and also the mediating role of creativity in the relationship of IV (TL & POS) in relation to DV (PSIO). Furthermore, this study tested a provisional model of public service innovation outcomes. Finally, all of the stages of the multiple regression, suggested by Berman and Wang (2012), were followed: a) model specifying; b) test of assumptions; c) modification of violation of assumptions; and d) regression model reporting and obtaining as per the results (Wang, 2012). Thus, this study has done by employing multiple regression techniques of statistics and later with path analysis.
3.7.5 Exploratory Factor Analysis (EFA)
EFA was applied in order to ensure construct validity. Other than measuring the validity, the identification of variables was one of the goals of the factor analysis.
The variables used in the model were dormant based on more than one indicator. In conducting factor analysis for acceptability, Bartlett’s test has to be statistically significant when the p<0.05. The test of adequacy sampling generates results, which
is known as the KMO index and spans between 0 to 1. It shows that when the result from the KMO is greater than 0.60, it is accepted as appropriate for the factor analysis. Moreover, in order to measure the adequacy of the factors’ internal reliability, the Cronbach’s alpha was calculated by using SPSS.
3.7.6 Regression Analysis
Regression analysis is for understanding the influences of the transformational leadership factors and perceived organizational support factors on the latent construct.
Examining the relationship, regression analysis employed SPSS 23.0 version. The target was to evaluate and analyze whether there was good enough evidence to show that there were significant predictors to estimate public service innovation outcomes;
moreover, whether there were influences of perceived organizational culture as a moderator and creativity as a mediator.
3.7.7 Structural Equation Modeling (SEM)
SEM is one of the powerful statistical models for stablishing measurement models and structural models, which help to identify complicated relationships in social science researches (Shook, Ketchen, Hult, & Kacmar, 2004). In the area of transformational leadership and public service innovation, Sarros et al. (2008) are the only researchers that have applied SEM. This statistical model is basically confirmatory rather than exploratory. It is suitable for providing information of the specific individual paths. Estimating each path, an interpretation was made for each relationship embodied in the model.
3.7.7.1 Partial Least Squares Regression
The present study used partial least squares structural equation modeling (PLS-SEM) using the SmartPLS program for testing the model hypothesis.
For behavioral research PLS can make a greater contribution for the analysis of causal relationships (Lowry & Gaskin, 2014). Further, it is also an influential multivariate technique that examines complex study problems, including overlooked variables and the complicated interaction of various variables. Through the bootstrapping technique PLS can calculate easily the p-values if the samples are independent and normally distributed data are not needed (Kline, 2011). This SmartPLS software was developed
by Ringle, Wende, and Will in 2005 and became famous for the outstanding application of the analysis of PLS-SEM. This program is well known for its ability to deal with small-size data, for example 100-200. Taking all of these strengths into consideration, the researcher used SmartPLS 3.0 for analyzing the data in the current research.
3.7.7.2 Path Analysis
Path analysis is used to analyze models that are more multifaceted, complicated, and complex than multiple regression (Streiner, 2005). It is also used to compare the different models in order to identify which is the best model and that best fits the data. More complex models may have more paths that may lead to more variables. This technique is known as path analysis.