The Value Of Kmo For Overall Matrix Essay

In this chapter, I will analyse the data according to the principles proposed, such as Likert’s scale. According to the data collected, I will come out with certain conclusions to predict the respondents’

As presented in Table 3, merging these three variables under one factor is statistically confirmed by the results of the factor analysis. The acceptable value for

The measuring of the variables is very difficult task, and selecting the measures of validation demonstrates the ability to ensure that the research will be reliable (Sechrest, 2005). I would think the by displaying the appropriate levels of measurement for variables ensures that the outcome will they will be done correctly (Frankfort-Nachmias & Nachmias, 2008). In addition, the reliable assessment ensures that the researchers' data does select for this study represents the connection with the variables (Frankfort-Nachmias & Nachmias, 2008). In reviewing the process I would say that the correct representation will affect the internal validity of this study (Sechrest, 2005). The purpose of this paper is to explain the levels of measurement

In this chapter the results obtained from the analysis of data collected (previous chapter) are discussed in relation to the aims and objectives of the study. The study attempts to answer the following research questions:

Correlational analysis were used to explore the relationships between the studies key variables, the results are indicated below (Table 2).

In order to complete the study, research questions were developed with specific variables in order to determine the results

Multiple assessment techniques were utilized to assess variable data and compare the reliability of that data for each individual variable.

II. Design: The authors designed their research by creating models with one to five classes which were analyzed. Distal factors (i.e., external variables) were not included in the estimation of the latent class models as this was an exploratory study.

In order to test the construct validity of each scale, bivariate correlation will be used, because this test is to find out the association among those different variables and how strong the association is. (Pallant,2010). At the same time, checking the association among the subscales is regarded as way to assess scale’s construct validity (ibid). In addition, most of the researchers believe that the dividing point of the statistical significance (p) to ensure the relationship between variables is .05. Namely, in 100 samples, there are 5 times chances to get relationship that are not appeared in the whole population. Specifically, p < 0.05 is set to ensure the expected relationship (Bryman and Bell, 2007). And all the

Data was analyzed by adapting SPSS (Statistical Package for Social Sciences) version fifteen.. Qualitative data was presented as number and percent. Comparison between groups was done by Chi-Square

Reliability and validity should be included in all research studies. This study will measure reliability by utilizing the test-retest reliability. The same measurement tool will be utilized multiple times, which will ask the same questions and follow the same research procedure. Validity will be measured by four criteria: face validity, content validity, criterion-related validity, and construct

The data used to perform analysis has been taken from the case study only. The factors available are:

The overall scores from the subscales were used. Leadership skills were measured with 68-item version of Socially Responsible Leadership Scale. The overall composite of all eight subscales (α = .93). Psychological well-being was measured with a 54-item Ryff Scales of Psychological Well-Being. The overall index was used and includes six subscales (α = .89). Intellectual engagement indicated with 18-item Need for Cognition Scale, which does not contain any subscales (α = .90). The Intercultural effectives was measured with 15-item Miville-Guzman Universality Scale. The overall score consisted of items from three subscales (α =

The Cronbach’s alpha of the sample responses came out to be as .784, which shows that our data is 78.4%. And the KMO test which is the sampling adequacy test came out to be as .691 which should be greater than .5, implies that factor analysis can be applied on this data.

The objective of this chapter is to describe the procedures used in the analysis of the data and present the main findings. It also presents the different tests performed to help choose the appropriate model for the study. The chapter concludes by providing thorough statistical interpretation of the findings.