PSY 101 - Assignment 2: Statistical Analysis of App-Based Game Survey

This assignment presents a statistical analysis of survey data collected from participants who played a sponsored app-based game. The analysis explores relationships between attitude toward the brand, electronic word-of-mouth (eWOM), and purchase intention. Descriptive statistics, including demographics such as gender, age, ethnicity, and income, are provided. The study utilizes t-tests, correlations, and regression analyses to address specific research questions (RQs). The findings indicate that attitude toward the brand significantly predicts eWOM, but eWOM does not significantly predict purchase intention. Gender differences were found in eWOM, but not in purchase intention or attitude toward the brand. The summary concludes that while participants showed interest in sharing information, their interest in purchasing the brand was not significantly influenced by eWOM. The study did not find a substantial relationship between “attitude toward the brand” and purchase intention, implying existence of other influencing factors.

Assignment #2
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Table of Contents
RQ1: Answer 1...........................................................................................................................2
RQ2: Answer 2...........................................................................................................................3
RQ3: Answer 3...........................................................................................................................3
RQ4: Answer 4...........................................................................................................................4
RQ5: Answer 5...........................................................................................................................4
RQ6: Answer 6...........................................................................................................................4
RQ7: Answer 7...........................................................................................................................5
Summary....................................................................................................................................5
References..................................................................................................................................6
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RQ1: Answer 1
Among 154 total participants in this study, fifty-nine percent of the respondents were
female and the average age within the sample was 27.57 years old (SD = 9.39). The average
age of 41% male participants was 26.43 years (SD = 7.15).
With regard to ethnicity, 23% were Anglo, 17% Hispanic, 7% Indian, 12% Asian,
20% African, 10% Multiracial, and 12% indicated International participants.
Twenty three percent of the respondents’ household income was between $30,000 and
$39,999, 16% had income between $40,000 and $49,999, 11% household earned between
$20,000 and $29,999, 10% household incomes were within $10,000 and $19,999, 9%
household incomes were within $50,000 and $59,999, a small 2% households earning was
below $10,000, and rest of 29% households earning were above $60,000.
RQ2: Answer 2
For 154 participants in the sample, average “attitude toward the brand” was 3.63 (SD
= 0.89), and average purchase intention was 3.21 (SD = 0.96). Pearson’s correlation test
was used to evaluate relationship between and purchase intention and “attitude toward the
brand”. Sample data indicated that no statistically significant relationship existed between the
two considered variables (r = - .12, p = .15). The p-value was greater than α= 0.05
implying that the negative correlation between the variables was statistically not significant
(Leech, 2012).
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RQ3: Answer 3
Whether “attitude toward the brand” can predict “electronic word-of-mouth”, a
simple linear regression was used (Chu, & Kim, 2011). The sample data indicated that
“attitude toward the brand” significantly (t = 3.46, p < .05) predicts 7.3% of the variance in
“electronic word-of-mouth” (eWOM) behavior (R = .27, F = 11.99, p < .05). The p-value
less than α= 0.05 indicated a statistically significant regression equation as: eWOM = 0.37
* “attitude toward the brand” + 2.49.
RQ4: Answer 4
Whether eWOM can predict purchase intention, a simple linear regression was used.
The sample data indicated that “electronic word-of-mouth” was not able to significantly
predict (t = -.79, p = .43) the purchase intention of people (R = .06, F = .62, p = .43). The p-
value was greater than α = 0.05, and eWOM was not able to significantly predict variation
in purchase intention of participants.
RQ5: Answer 5
An independent sample t-test was used to find gender difference for purchase
intention. Average purchase intention for 63 males in the sample was 3.22 (SD = 0.97), and
that of the 91 females was 3.21 (SD = 0.95). There was no significant difference in variances
of purchase intention between the two genders (F = 0.34, p = 0.56). Also, no statistically
significant difference between average purchase intention for male and females was noted (t
(152) = 0.85, p = 0.93). The p-value was greater than α= 0.05, indicating no significant
difference between average purchase intention of two genders.
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RQ6: Answer 6
An independent sample t-test was used to find gender difference for e-word-of-
mouth. Average eWOM for 63 males in the sample was 3.29 (SD = 1.29), and that of the 91
females was 4.21 (SD = 1.03). There was significant difference in variances of eWOM
between the two genders (F = 10.29, p < 0.05). Therefore, degree of freedom was adjusted to
113.50 from 152. A statistically significant difference between average eWOM for male and
females was identified (t (113.5) = -4.74, p < 0.05). The p-value was less than α= 0.05,
indicating a significant difference between average eWOM of two genders (Lakens, 2013).
RQ7: Answer 7
An independent sample t-test was used to find gender difference for “attitude toward
the brand”. Average “attitude toward the brand” for 63 males in the sample was 3.52 (SD
= .96), and that of the 91 females was 6.70 (SD = .84). There was no significant difference in
variances of “attitude toward the brand” between the two genders (F = 3.25, p = 0.07). No
statistically significant difference between average “attitude toward the brand” for male and
females was identified (t (152) = -1.18, p = 0.24). The p-value was greater than α = 0.05,
indicating no significant difference between average “attitude toward the brand” of two
genders.
Summary
The present study investigated the response of 154 participants on attitude toward an
app-based game, likelihood to share information (eWOM), and purchase intention for the
brand. Demographic particulars were also collected for constructing a meticulous exploration
about the intention of the participants for purchasing the brand (Josiassen, Assaf, & Karpen,
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2011). The study did not find any substantial relationship between “attitude toward the
brand” and purchase intention, implying existence of other influencing factors. “attitude
toward the brand” was found to be a considerable factor predicting likelihood to share
information. But, interestingly, likelihood to share information was not a significant factor
explaining the purchase intentions, and the significant difference between likelihood to share
information for male and females could be considered as the underlined cause. Also, no
gender wise difference in purchasing intention and “attitude toward the brand” was identified
among the participants. Overall assessment revealed that participants were not expressively
interested in buying the brand, but outlook toward the brand influenced their aspiration to
share information with others.
References
Chu, S. C., & Kim, Y. (2011). Determinants of consumer engagement in “electronic word-of-
mouth” (eWOM) in social networking sites. International journal of Advertising,
30(1), 47-75.
Josiassen, A., Assaf, A. G., & Karpen, I. O. (2011). Consumer ethnocentrism and willingness
to buy: Analyzing the role of three demographic consumer characteristics.
International Marketing Review, 28(6), 627-646.
Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: a
practical primer for t-tests and ANOVAs. Frontiers in psychology, 4, 863.
Leech, N. L. (2012). Writing mixed research reports. American Behavioral Scientist, 56(6),
866-881.
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