DATA ANALYSIS TECHNIQUE

The data collected from the respondents were coded and compiled using the Statistical Package for Social Sciences (SPSS) version 19.0 software and quantitative method was exercised in this study to analyze the collected data. A number of analyses will be used as a part of the data analysis technique. Zikmund et al. (2010), stated that “the appropriate analytical technique for data analysis will be determined by management’s information requirements, the characteristics of the research design, and the nature of the data gathered” (Zikmund et al., 2010). The brief descriptions of the different types of analysis performed were as follows:

3.10.1 Data Screening

Before conducting further analysis on the data set obtained, it is important to conduct data screening to check for data errors. Mistakes could happen during the data entry process and the mistakes would affect the analysis result greatly as some analysis are very sensitive to what are known as ‘outliers’; that is, values that are well below or

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well above the other scores (Pallant, 2011). Other process involved during the screening stage were identifying outliers and decide on how to deal with it and screening for normality of the data set.

3.10.2 Missing Data

Pallant (2011) point out that when a research carried out involved participation of human beings, there is always the possibility of getting incomplete data in every case. Therefore, it is essential to check the data file for any missing data and to carefully decide on how to deal with the missing data. Two considerations that should be taken into account when encountering a missing data are:

i. Whether the missing values are happening randomly or ii. Whether there is some systematic pattern.

The decision to choose for appropriate procedures on how to deal with the missing values is very important as it can have a dramatic effects on the statistical analysis conducted.

3.10.3 Outliers

Some analysis result are very highly affected by outliers. To identify outliers, Pallant (2011) had suggested looking into details as below:

i. Details on the computed histogram. Look at the tails of the distribution. Data points which sits on their own are potential outliers.

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ii. Inspecting the boxplot will also help to identify outliers. Any scores that SPSS considers are outliers appear as little circles with a number attached (this is the ID number of the case). If there are points like this, researcher must decide what to do with them.

3.10.4 Normality Test

Normality assume that the distribution of scores on the dependent variable is ‘normal’ (Pallant, 2011). Normality assessment can be done by obtaining the skewness and kurtosis value. Skewness refers to the symmetry of a distribution, that is, a variable whose mean is not in the centre of the distribution. Kurtosis relates to the peakedness of a distribution. When a distribution is normal, the values of skewness and kurtosis should be close to zero. For graphical method, normality can also be determined by examining the residual plots. If the assumption is met, the residuals should be normally and independently distributed (Tabachnick & Fidell, 2001). Normality of a data can also checked by looking at histogram, boxplot, normal Q-Q plot and also the detrended normal Q-Q plot of each variable tested.

3.10.5 Factor Analysis

A group of statistical technique or better known as factor analysis is used to analyse a large number of related variables and to explore the underlying structure of this set of variables. It is useful in reducing a large number of related variables to a smaller, more manageable, number of dimensions or components. Factor analysis can also be used to reduce a large number of related variables to a more manageable number, prior to using them in other analysis such as multiple regression or multivariate analysis of variance.

114 3.10.6 Reliability Analysis

Reliability test was conducted to test the reliability of the research instrument used in the research. Generally, reliability test is to determine the degree to which a test is consistent and stable in measuring what it is intended to measure. One of the aspect of reliability that can be measured is internal consistency. This is the degree to which the items that make up the variables is consistent and stable to produce the intended result when analysis is performed. The reliability test is also conducted to identify whether all items for all respective variables in the questionnaire are highly related and reliable. Internal consistency can be measured in a number of ways. The most commonly used statistic is Cronbach’s coefficient alpha. Pallant (2011), stated that values range from 0 to 1 with higher values indicating greater reliability (Pallant, 2011). As cited by Pallant (2011), Nunnally (1978) recommends a minimum level of 0.7 (Pallant, 2011). Sekaran (2003), specified that the closer Alpha value to 1, it represented a high level of reliability (Cronbach’s Alpha = > 0.90). If the Alpha value is less than 0.6, it may be predicted that instrument used in the study had a low reliability (Cronbach’s Alpha = < 0.60). If value of Alpha is more than 0.7 (Cronbach’s Alpha = 0.7< 0.9), it indicates the instrument is good and acceptable reliability.

3.10.7 Descriptive Analysis

A descriptive analysis is also be conducted to analyse the collected data. As stated by Pallant (2011), descriptive statistics have a number of uses. These include to, describe the characteristics of the sample in the Method section of the report; check the variables for any violation of the assumptions underlying the statistical techniques that will be used to address your research questions;address specific research

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questions (Pallant, 2011). The purpose of conducting descriptive analysis was to obtain the data for frequency distribution, measures of central tendency and measures of dispersion of variability. For the purpose of this study, descriptive statistic was used to describe and analyze the basic feature of the data in the study namely demographic profile, internet and social media usage pattern of the respondents.

3.10.8 Correlation Analysis

The Pearson Product-Moment Correlation also known as Pearson Correlation Coefficient (PCC) is performed in this research among the variables developed in each hypothesis to determine the scope and importance of any relationships prior to performing regression analysis on the study results. To determine the scale and the direction of the relationships among variables, the Pearson product moment correlation coefficient was used. Pearson correlation coefficients (r) can range from – 1 to +1. The higher the positive data obtained for example. Table 3.4 below shows the coefficient scale and the relationship strength.

Table 3.3

Coefficient Scale and Relationship Strength

Coefficient Scale Relationship Strength

0.91 – 1.00 0.71 – 0.90 0.41 – 0.70 0.21 – 0.40 0.01 - 0.20 Very strong Strong Moderate Weak Very weak

116 3.10.9 Regression Analysis

To confirm whether the developed hypothesis are true, the most suitable technique to use is multiple regression as it can measure the linear association between a dependent and an independent variable (Zikmund, 2003). Multiple regression is not just one technique but a family of techniques that can be used to explore the relationship between one continuous dependent variable and a number of independent variables or predictor. In other word, multiple regressions are used to learn about the relationship between several independent variables and a dependent variable.