Advance Quantitative Analysis

Question 1

A. Distinguish between a quantitative study and a qualitative study.
B. When using a Quantitative method, there is a procedure that guides the researcher. Elaborate the Quantitative procedure with examples.
C. Critically discuss the issues of Outliers in data analysis and state its effect on statistical data analysis.

Question 2

A. There are different measurement of variables in Quantitative data analysis namely Scale, Ordinal and Nominal. Suppose you are conducting data analysis that includes the following variables: Age, Income, Level of Satisfaction, Region and Zip Code. Discuss the appropriate measurement to use for each variable and why.

B. In conducting Quantitative data analysis, there are 4 main steps and they are:
-Getting ready for data analysis
-Getting a ‘Feel’ for the data -Testing the goodness of the data
-Testing the hypotheses.
Evaluate each statement with examples of the software package that aids in carrying out every step.
Question 3
In a research conducted about Motivation and Job Satisfaction, the researcher asked about the Demographic profile of the respondents, and the results are given below in the tables. You are required to interprete the resultsand write a detailed data analysis report.
Table 1: Gender
Gender
Frequency Percent Valid Percent Cumulative
Percent
Valid Male 17 56.7 56.7 56.7
Female 13 43.3 43.3 100.0
Total 30 100.0 100.0

Table 2: Job Level
Joblevel
Frequency Percent Valid Percent Cumulative
Percent
Valid Operation Manager 16 53.3 53.3 53.3
Middle Manager 12 40.0 40.0 93.3
Senior Manager 2 6.7 6.7 100.0
Total 30 100.0 100.0

Question 4

Based on the research framework and the output tables given, you are required to: A. Identify with justification whether the given model is reflective or formative.

B. Evaluate the model by listing itshypotheses.
C. Interprete and write a comprehensive data analysis report with references. Your interpretation should comprise both the measurement model and the equation model. You should evaluate the figures in the tables and state whether for each technique, they are within the acceptable threshold or not.

Figure 1: Research Framework
Table 1: Discriminant Validity-HTMT
Constructs COM PEOU
COM – –
PEOU 0.569 –

Table 2: Construct Reliability and Validity
Constructs Cronbach’s Alpha Composite
Reliability Average Variance Extracted
ACC – – –
COM 0.704 0.836 0.633
ESQ – – –
PEOU 0.756 0.836 0.508

Table 3: Outer VIF Values
Constructs VIF
Com_2 4.704
Com_3 4.289
Com_5 1.226
ESQ_1 1.613
ESQ_2 2.791
ESQ_3 3.051
ESQ_4 2.661
ESQ_5 1.585

Table 4: T Statistics & P values (Indirect Relationship)
Relationships T Statistics (|O/STDEV|) P Values
COM -> ACC – –
ESQ -> ACC – –
PEOU -> ACC 6.527 0.000
PEOU -> ESQ – –

Table 5: T Statistics & P values
Relationships T Statistics P Values
COM -> ACC 5.739 0.000
ESQ -> ACC 7.353 0.000
PEOU -> ACC 2.575 0.010
PEOU -> ESQ 30.003 0.000