INTI University MGT6208: Quantitative Data Analysis Methods Report

This report evaluates quantitative data analysis methods employed in two scholarly articles focusing on brand equity within a business context. The report examines the methodologies used, including sample size determination, data analysis procedures, and the rationale behind the selection of correlation analysis. It assesses the strengths and weaknesses of the approaches taken in each article, considering factors like the impact of consumer loyalty and brand awareness on brand equity. Furthermore, the report offers suggestions for alternative methodologies, such as linear regression, and discusses the limitations of the studies, including sample size concerns and the scope of variables considered. The analysis concludes with a summary of the findings and their implications for understanding brand equity and marketing strategies.

Running head: QUANTITATIVE DATA ANALYSIS METHODS
Quantitative Data Analysis Methods
Name of the Student:
Name of the University:
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Table of Contents
Introduction................................................................................................................................2
Sample Size................................................................................................................................2
Data Analysis Procedure............................................................................................................3
Reasons and Advantages of Correlation analysis......................................................................3
Suggestions................................................................................................................................4
Conclusion..................................................................................................................................5
Reference....................................................................................................................................6

2QUANTITATIVE DATA ANALYSIS METHODS
Introduction
The quantitative data analysis methods is very important in research to achieve the
research objectives. Quantitative data analysis uses raw numbers collected either from a
survey that is primary data or from authentic journals and government data that is secondary
data or both the primary and secondary data. These are used in the analysis to find the
evidence to support or reject the hypothesis formed to attain the research objectives. To
evaluate the quantitative analysis methods, two articles are selected on brands in business
context which has used the quantitative methodology. The name of the articles are mentioned
below:
 Article 1: “Impact of Consumer’s Loyalty and Attachment o Brand Equity (A of
Beverage Sector of Pakistan)”
 Article 2: “Impact of Brand Awareness and Loyalty on Brand Equity”
Sample Size
The proper way to choose a sample size is to follow the standard equation of
necessary sample size which is mentioned below:
Necessary Sample ¿ ( Z−score )2∗( Standard Deviation )∗( 1−Standard Deviation )
( Margin of error )2
Here, Z-score is chosen on the basis of selection of confidence interval or the
significance level at which the researcher want to be confident about the results. The safe
decision about standard deviation is to use 0.5 as this ensures that the sample will be large
enough and a larger sample provides better results (Chow et al., 2017).
Article 1: This article collects data by taking responses from 200 respondent on a
questionnaire prepared on the basis of literature review of the study. The data is wide and it is
collected explicitly. The paper is discussing the results on 95% and 99% confidence interval

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and if it is assumed that the standard deviation is chosen to make the sample size large
enough for the study then the margin of error is quite higher (Malterud, Siersma & Guassora,
2016).
Article 2: This researcher conducted survey by following a random sampling technique and
collected 210 samples. After removing the missing values from the sample set, there were
200 samples that has been used in the analysis. The value of chron batch alpha was estimated
to be 0.88 which is greater than 0.7 which reflects better validity and reliability.
Data Analysis Procedure
There are a number of analysis tools and techniques to find or extract useful
information form the data (Houghton et al. 2015). Let’s discuss the analysis procedures used
in the articles.
Article 1: The article used the bivariate correlation analysis to check the relationship between
“brand equity” which is dependent variable and “consumer loyalty” and “attachment with
brand” which are independent variable (Liow, 2015). The correlation analysis is a statistical
method that helps to evaluate the strength of a relationship between two continuous variables.
The study finds that the consumer loyalty has an influence on brand equity and attachment
with brand has significant influence on the brand equity.
Article 2: The article used the correlation analysis to check the relationship of brand equity
(dependent variable) with the brand awareness and brand loyalty (independent variable). The
study finds that there exist a significant positive relationship between brand awareness and
brand equity and there is a positive relationship between customer loyalty and brand equity.
Reasons and Advantages of Correlation analysis
The analysis techniques are always chosen according to the research objectives or on
the basis of the requirements to attain the research aim. For example, linear regression

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analysis is used to predict or forecast and correlation analysis is used to check the strength of
relationship between variables.
Article 1: The objective of the article was to find the impact of the brand loyalty on brand
equity and impact of brand attachment on brand equity. The correlation analysis is
appropriate to present the strength of the relationship between two variables. The correlation
coefficient of consumer loyalty and brand equity is statistically significant with p-value 0.000
and presents a significant relationship. Similarly, the correlation coefficient of attachment
with brand and brand equity is statistically significant with p-value 0.000 and correlation
coefficient is 0.545 which presents moderate strength of relationship (DeFusco et al. 2015).
Article 2: The research question of the article is how brand awareness affect the brand
equity and does the consumer loyalty about the brand have its impact on brand equity. The
correlation analysis can answers these questions by evaluating the strength of the relationship
between the variables. The correlating coefficient between consumer loyalty and brand equity
is 0.147 with p-value 0.00 that explains that the consumer loyalty significantly affects the
brand equity but have a weak impact. Similar impact is observed for the brand awareness.
Suggestions
Article 1: The article wants to know how much brand loyalties affect equity positively or
negatively and does the brand attachment have its impact on brand equity. The researcher
used the correlation analysis to establish the evidence accordingly. However, linear
regression can also do the same. The regression analysis gives the multiple R2 value which
shows the relationship with error. If the value of multiple R2 is 1 then there is no error. It also
provides an equation where the impact of independent variable can be measured numerically
which is represented by the coefficient of the independent variable. The significance of the
coefficient is described by the t-stat and p-value (Darlington & Hayes 2016). The sign of the

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coefficient can explain the positive and negative relation between the dependent and
independent variable.
Article 2: The article wants to analyse that how brand awareness affect brand equity and
does consumer loyalty about the brand have its impact on brand equity. Here the brand equity
is dependent variable and the other variables independent. The Linear Regression model can
be used to answer the research questions of the article. The result table contains the Multiple
R2 value, F-stat and associated p-value. The coefficient of independent variable that presents
the impact of independent variable on the dependent variable.
Limitation of the Studies
Article 1: The sample size is not large enough and the margin of error is quite higher. So,
the researcher should have collected more data to reduce the margin of error. There are more
variable that can affect the brand equity like addictive attachment, friendship attachment,
behavioural loyalty, attitudinal loyalty and nostalgic loyalty.
Article 2: The data collection area is very small so the results are true for that areas only not
for the whole population. There are more variable that can affect the brand equity which are
not discussed in the article.
Conclusion
Article 1: There is a positive influence of brand loyalty and attachment of the consumer on
brand equity. This article supports that the brand in the beverage sector plays a significant
role in the success of entry of new products.
Article 2: The article presents the consumer based brand equity measurement with the
incorporation of brand awareness and brand loyalty. The brand awareness and brand loyalty
presents a positive and significant relation with the brand.

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Reference
Chow, S. C., Shao, J., Wang, H., & Lokhnygina, Y. (2017). Sample size calculations in
clinical research. Chapman and Hall/CRC.
Darlington, R. B., & Hayes, A. F. (2016). Regression analysis and linear models: Concepts,
applications, and implementation. Guilford Publications.
DeFusco, R. A., McLeavey, D. W., Pinto, J. E., Runkle, D. E., & Anson, M. J.
(2015). Quantitative investment analysis. John Wiley & Sons.
Houghton, C., Murphy, K., Shaw, D., & Casey, D. (2015). Qualitative case study data
analysis: An example from practice. Nurse researcher, 22(5).
Liow, K. H. (2015). Volatility spillover dynamics and relationship across G7 financial
markets. The North American Journal of Economics and Finance, 33, 328-365.
Malterud, K., Siersma, V. D., & Guassora, A. D. (2016). Sample size in qualitative interview
studies: guided by information power. Qualitative health research, 26(13), 1753-
1760.
Plümper, T. (2017). Advanced Quantitative Data Analysis: Methodology for the Social
Sciences.