Differences Between Qualitative and Quantitative Statistical Methods

This assignment explores the differences between qualitative and quantitative research methods, highlighting their distinct approaches to data collection and analysis. It contrasts the subjective nature of qualitative research with the objective approach of quantitative methods, emphasizing the use of purposive versus random sampling, and inductive versus deductive reasoning. The assignment then presents a quantitative analysis of sample data, including descriptive statistics for age, weight, and CGPA, along with a breakdown of personality types and occupations. The analysis further includes a correlation matrix showing relationships between variables and an independent samples t-test comparing CGPA for introverts and extroverts. Finally, it presents a regression analysis to predict CGPA, concluding that the model is not significant. The assignment includes tables, graphs, and references to support the findings.

Statistics
Student name:
University
Lecturer name:
25th October 2017

(i) Discuss at least four (4) clear differences between qualitative and quantitative
methods. Provide sufficient and relevant examples
Solution
 Qualitative research method is a technique of inquiry that develops understanding
on human and social sciences, to find the way people think and feel. On the other
hand, a quantitative research method is a scientific and empirical research method
that is used to generate numerical data, by employing statistical, logical and
mathematical technique.
 Qualitative research method follows a subjective approach as the researcher is
intimately involved, whereas the approach of quantitative research method is
objective, as the researcher is uninvolved and attempts to precise the observations
and analysis on the topic to answer the inquiry.
 Qualitative research method is based on purposive sampling, where a small
sample size is selected with a view to get a thorough understanding of the target
concept. Quantitative research method on the other hand relies on random
sampling; wherein a large representative sample is chosen in order to extrapolate
the results to the whole population.
 The hypothesis is generated in qualitative research by inductive reasoning.
Inductive reasoning refers to a logical process where multiple premises that are
believed to be true or are rather found to be true most of the time, are combined to
obtain a specific conclusion. On the contrary, the hypothesis is tested by
deductive research in quantitative reasoning. In a deductive reasoning, the
general premises or statements are used to form a specific conclusion.

 Qualitative Research is conducted with the aim of exploring and discovering ideas
used in the ongoing processes. As opposed to quantitative research the purpose is
to examine cause and effect relationship between variables.
(ii) Classify and analyze the data above using quantitative methods correctly. Show
clearly the raw result of the data analysis in the appendix of the assignment.
Solution
The above graphs represent the histograms for the age, weight and CGPA. As can be seen, the
graphs shows that the data are not normally distributed for all the three variables.

iii) Descriptive statistics
We present the descriptive statistics for the numerical variables. As can be seen, the average
CGPA is 3.305 while the average age is 40.5 and the average weight of the participants is
88.633.
Table 1: Descriptive Statistics
Age (years) Weight (kg) CGPA
Mean 40.500 88.633 3.305
Standard Error 2.341 3.789 0.083
Median 37.500 91.000 3.260
Mode 30.000 114.000 3.260
Standard Deviation 12.822 20.752 0.457
Sample Variance 164.397 430.654 0.209
Kurtosis 0.984 -0.873 0.477
Skewness 1.084 -0.177 -0.517
Range 54.000 76.000 1.910
Minimum 23.000 50.000 2.060
Maximum 77.000 126.000 3.970
Sum 1215 2659 99.15
Count 30 30 30
Personality

As can be seen, majority of the participants were introverts (70%, n = 21) while the extroverts
were represented by 30% (n = 9).
Occupation
Majority of the respondents (27%, n = 8) were teachers, the least represented were the lawyers
(7%, n = 2).
iv) Clear and detailed write-up
In the preceding section, a write-up of the analysis is presented. Different statistical tests are used
to analyze the data.
Correlation matrix
The table below shows the Pearson correlation coefficients for the three variables. As can be
seen, all the three variables are negatively related. The magnitude of the coefficients shows a
weak relationship.
Table 2: Correlation matrix
Age (years) Weight (kg) CGPA

Age (years) 1
Weight (kg) -0.2428 1
CGPA -0.02385 -0.29457 1
Comparison of CGPA for the Introverts and the Extroverts
Group Statistics
Personality2 N Mean Std. Deviation Std. Error Mean
CGPA Introvert 21 3.3252 .35305 .07704
Extrovert 9 3.2578 .66516 .22172
Independent Samples Test
Levene's Test for
Equality of
Variances
t-test for Equality of Means
F Sig. t df Sig. (2-
tailed)
Mean
Differenc
e
Std.
Error
Differenc
e
95% Confidence
Interval of the
Difference
Lower Upper
CGPA
Equal variances
assumed
4.464 .044 .365 28 .718 .06746 .18493 -.31134 .44626
Equal variances not
assumed
.287 9.990 .780 .06746 .23473 -.45561 .59053
An independent samples t-test was done to compare the mean sum CGPA for the Introverts and
the Extroverts. Results showed that the average sum CGPA for the Introverts (M = 3.33, SD =
0.35, N = 21) had no significant difference with the average sum CGPA for the Extroverts (M =
3.26, SD = 0.67, N = 9), t (28) = 0.365, p > .05, two-tailed. The difference of 0.067 showed an
insignificant difference. Essentially results showed that the average sum CGPA for the
Introverts and Extroverts were no significantly different.

Prediction of CGPA
The regression analysis to predict the CGPA using age and personality. As can be seen, the
model seems not to be significant and as such cannot predict the CGPA. All the explanatory
variables are insignificant in the model.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .075a .006 -.068 .47247
a. Predictors: (Constant), Personality2, Age (years)
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression .034 2 .017 .076 .927b
Residual 6.027 27 .223
Total 6.061 29
a. Dependent Variable: CGPA
b. Predictors: (Constant), Personality2, Age (years)
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 3.369 .301 11.191 .000
Age (years) -.001 .007 -.030 -.155 .878
Personality2 -.070 .189 -.071 -.370 .714

a. Dependent Variable: CGPA
References
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2002). Applied multiple regression/correlation
analysis for the behavioral sciences (3rd ed.).
Dean, S., & Illowsky, B. (2009). Descriptive Statistics: Histogram.
Howitt, D., & Cramer, D. (2008). Statistics in Psychology.
Mahdavi , D. B. (2013). The Non-Misleading Value of Inferred Correlation: An Introduction to
the Cointelation Model.
Székely, G. J., & Bakirov, N. K. (2007). Measuring and testing independence by correlation of
distances. Annals of Statistics, 35(6), 2769–2794.
Tofallis, C. (2009). Least Squares Percentage Regression. Journal of Modern Applied Statistical
Methods, 7, 526–534.

Appendix
Descriptive Statistics
Age (years) Weight (kg) CGPA
Mean 40.500 88.633 3.305
Standard Error 2.341 3.789 0.083
Median 37.500 91.000 3.260
Mode 30.000 114.000 3.260
Standard Deviation 12.822 20.752 0.457
Sample Variance 164.397 430.654 0.209
Kurtosis 0.984 -0.873 0.477
Skewness 1.084 -0.177 -0.517
Range 54.000 76.000 1.910
Minimum 23.000 50.000 2.060
Maximum 77.000 126.000 3.970
Sum 1215 2659 99.15
Count 30 30 30