Respond to the following short answer questions in Chapter 7 from the Morgan, Leech, Gloeckner, &
Barrett textbook:
D.5.7.1 In Output 7.1: (a) What do the terms “count” and “expected count” mean? (b) What does the
difference between them tell you?
D.5.7.2 In Output 7.1: (a) Is the (Pearson) chi-square statistically significant? Explain what it means. (b)
Are the expected values in at least 80% of the cells ≥ 5? How do you know? Why is this important?
D.5.7.4 Because father’s and mother’s education revised are 3-level variables with at least ordinal data,
which of the statistics used in Problem
D.5.7.3 is the most appropriate to measure the strength of the relationship: phi, Cramer’s V, or Kendall’s
tau-b? Interpret the results. Why are tau-b and Cramer’s V different?
D.5.7.5 In Output 7.4: (a) How do you know which is the appropriate value of eta? (b) Do you think it is
high or low? Why? (c) How would you describe the results?
DISCUSSION ASSIGNMENT INSTRUCTIONS
The student will complete 8 short-answer discussions in this course and 1 long-answer
Integrating Faith and Learning discussion. In the thread for each short-answer discussion the
student will post short answers to the prompted questions. The answers must demonstrate
course-related knowledge and support their assertions with scholarly citations in the latest APA
format. Minimum word count for all short answers cumulatively is 200 words. The minimum
word count for Integrating Faith and Learning discussion is 600 words. For each thread the
student must include a title block with your name, class title, date, and the discussion forum
number; write the question number and the question title as a level one heading (e.g. D1.1
Variables) and then provide your response; use Level Two headings for multi part questions (e.g.
D1.1 & D1.1.a, D1.1.b, etc.), and include a reference section.
The student must then post 1 reply to another student’s post. The reply must summarize the
student’s findings and indicate areas of agreement, disagreement, and improvement. It must be
supported with scholarly citations in the latest APA format and corresponding list of references.
The minimum word count for Integrating Faith and Learning discussion reply is 250 words.
Top simplify the explanation above each forum is very short every week. I would shoot for 300 words
total with two APA format references less than 5 years old. Don’t spend too much time on this the entire
discussion is only 25 points with the reply compared to previous classes of 125 points. I’m expecting the
reply to be included in the one hour I’m paying for here. The reply again is 200 words with a required 1
APA reference that’s less than 5 years old. I will send over the reply in a couple days once the students
post their Discussions. Thanks!
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Author Note
Joe Lipiec
I have no known conflict of interest to disclose.
Correspondence concerning this article should be addressed to Joe Lipiec.
Email: jlipiec@liberty.edu
D.5.7.1 In Output 7.1:
(a) What do the terms “count” and “expected count” mean?
According to Morgan et al. (2020), the difference between the terms “count” and “expected
count is used to represent or explain the outcome expected by chance and the actual results.
(b) What does the difference between them tell you?
Put another way, “expected count” is the number one might believe is attributable to chance
and “count” is the actual result based on statistical analysis.
D.5.7.2 In Output 7.1:
(a) Is the (Pearson) chi-square statistically significant? Explain what it means.
At .056, the chi-square test is not statistically significant. According to Morgan et al. (2020) p
values less than .05 are statistically significant. Statistical significance determines whether or not
the statistic can be relied upon for statistical analysis.
(b) Are the expected values in at least 80% of the cells ≥ 5? How do you know? Why is this
important?
It appears that the expected values are in at least 80% of the cells greater than or equal to 5. In
fact, it appears expected values are in 100% of the cells greater than or equal to 5. According to
Morgan et al. (2020) expected values of less than 5 are deemed too generous.
D.5.7.3 In Output 7.2:
(a) How is the risk ratio calculated? What does it tell you?
The risk ratio is calculated by dividing the percentage of the first value of a dichotomous
variable by the percentage of the second value of a dichotomous variable. The risk ratio is used
to present a comparison of two dichotomous variables in statistical measurement to determine
statistical significance (Morgan et al., 2020.)
(b) How is the odds ratio calculated and what does it tell you?
The odds ratio is calculated by dividing one risk ratio by another, so it is a ratio of ratios. It is
used as an effect size measure, also known as risk potency measure (Morgan et al., 2020.)
(c) How could information about the odds ratio be useful to people wanting to know the
practical importance of research results?
A large odds ratio could be interpreted as indicating the event or data under analysis is either
very rare or very common, depending on the context of the test being performed (Morgan et al.,
2020.)
(d) What are some limitations of the odds ratio as an effective size measure?
One limitation of the odds ratio as an effective measure is that there is no general consensus
about what is considered a large ratio. Another limitation is that an odds ratio can be performed
only two dichotomous variables (Morgan et al., 2020.)
D.5.7.4 Because father’s and mother’s education revised are 3-level variables with at least
ordinal data, which of the statistics used in Problem 7.3 is the most appropriate to measure
the strength of the relationship: phi, Cramer’s V, or Kendall’s tau-b? Interpret the results.
Why are tau-b and Cramer’s V different?
Kendall’s tau-b is the most appropriate statistic to measure the strength of the relationship
between father’s revised education and mother’s revised education. According to Morgan et al.
(2020) the phi and Cramer’s V statistics are appropriate for nominal variables. Kendall’s tau-b,
however, can be used to determine association in those circumstances that both variables are
ordinal.
D.5.7.5 In Output 7.4:
(a) How do you know which is the appropriate value of eta?
The appropriate value is eta is selected based on the researcher’s determination or needs of
which variable is dependent.
(b) Do you think it is high or low? Why?
For both the math courses taken and academic track variables presented in 7.4, the eta values
are closer to the low end but I would describe them as moderate. According to Morgan et al.
(2020), eta values range from zero to approximately 1.0. The closer a value is to 1.0, the stronger
one could conclude is the association between the two variables.
(c) How would you describe the results?
Looking to determine the strength of association between academic track and number of math
courses taken, the eta of .33 indicates a moderate association that fast track students probably
took more math classes than students in the regular track.
References
Morgan, G. A., Gloeckner, G. W., Leech, N. L., & Barrett, K. C. (2020). IBM SPSS for
introductory statistics: Use and interpretation. Routledge.
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BUSI820 Discussion 5
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Discussion 5
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BUSI820 Discussion 5
D.5.7.1 In Output 7.1: (a) What do the terms “count” and “expected count”
mean?
As seen in output 7.1, the term' count' shows the actual result of the number of students who got
either a less A-B grade or a most A-B grade from the math test with the academic track. The
academic track comprises of the fast track and regular track (Morgan et al., 2019). On the other
hand, the 'expected count' represents the estimated result/number of students to get a lower A-B
grade or most A-B grade before taking the math test.
D.5.7.1 (b) What does the difference between them tell you?
The difference between the expected count and count results show that the students estimated to
get a lower grade in the math test within the academic track were those from the regular track.
From the actual results, we see that the higher percentage of students that got the lower grade in
the math test were from the fast track, who were previously estimated to be the few in getting low
grades (Morgan et al., 2019). For the high performance, the difference seen between the expected
count and the count results is the same as that of the lower grade. We see those students expected
to pass the test are those from the fast track, but the count results show that students from the
regular track passed the test most. This tells us that you may expect a particular outcome, but the
result becomes vice versa.
D.5.7.2 In Output 7.1: (a) Is the (Pearson) chi-square statistically significant? No, the
chi-square is not statistically significant (Morgan et al., 2019). Explain what it means. The chi-
square being not statistically significant means that you cannot tell the certainty of whether the fast
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track and the regular track students are systematically different on the circumstance of having
either high or low math grades.
D.5.7.2 (b) Are the expected values in at least 80% of the cells ≥ 5? Yes, actually, all the
expected values are more than five. How do you know? From the cells with expected count values
in output 7.2, there is no count less than five (Morgan et al., 2019). Why is this important? This
is because the chi-square would not be applicable if there were those values less than five, as it
requires only more than five values for the expected counts.
D.5.7.4 Because father’s and mother’s education revised are 3-level variables with at
least ordinal data, which of the statistics used in Problem 7.3 is the most appropriate
to measure the strength of the relationship: phi, Cramer’s V, or Kendall’s tau-b? Due
to the ordinal state of the two variables, Kendall's tau-b is the most appropriate for this case.
Interpret the results. The value of tau-b is .572, which is high, the statistical significance is .000;
therefore, the association between the father's and mother's education is strong (Morgan et al.,
2019). Why are tau-b and Cramer’s V different? The tau-b and the Cramer's V differ in that the
tau-b measures the strength of association between two ordinal data while the Cramer's V measures
the strength of association between two nominal data with one or both having three or more levels.
D.5.7.5 In Output 7.4: (a) How do you know the appropriate value of eta?
To know the appropriate value of eta in output 7.4, you divide the number of math courses taken,
which are 6, from the total count result of both fast and regular tracks individually, and then add
the results for the two tracks.
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D.5.7.5 (b) Do you think it is high or low? The eta value in output 7.4, which is .33, is low.
Why? The reason is that the values closer to +1.0 are the ones taken with the high association, as
eta ranges from zero to +1.0
D.5.7.5 (c) How would you describe the results? The students on the fast track took most or
all of the math courses. This is proved by the fast track column with only two courses with negative
discrepancies between the actual and expected counts (Morgan et al., 2019). The regular track
column had four courses with negative discrepancies; hence students took fewer math courses.
References
Morgan, G. A., Barrett, K. C., Leech, N. L., & Gloeckner, G. W. (2019). IBM SPSS for
Introductory Statistics (6th Edition). Taylor & Francis.
https://mbsdirect.vitalsource.com/books/9781000011753
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Reply to BUSI820 Discussion 5
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Reply to BUSI820 Discussion 5
Hey Joe Lipiec,
I would be wrong disagreeing with you on what count and expected count mean. Expected
count indeed shows the results by chance, while count shows the actual results. The problem with
your answer is that you never specified which is which. On problem 1b, I am afraid I have to
disagree with you for not giving the needed solution to the problem. The problem required to know
what the difference between count and expected count tells us. A difference that the two tell us is
that the estimated count/expected count may be significantly off-track compared to the actual
count. I agree with you that for problem 2a, the chi-square is not statistically significant because it
has a p-value of .056, and we know that statistical significance is .05 and below (Morgan et al.,
2019). For problem 2b, I also agree with your answer, Joe, because from output 7.1 in the book,
we saw clearly that all expected counts were above five. For the whole of problem 3, I tend to
agree with your answers as your explanations justify them well. For problem four, I agree with
you, Joe, that Kendall's tau-b was the most appropriate statistic for the case. This is because the
two variables in check were both ordinal. I also agree that the phi and Cramer's are both most
appropriate for nominal variables. For problem 5a, I agree with you that knowing the proper eta
value depends on the researcher's choice for which variable to make dependent. The eta value in
output 7.4 is indeed low as eta ranges from zero to one, and the closer the value is to one, the
higher the degree of association the variables get. Finally, I agree with you, Joe Lipiec, that from
the eta value in output 7.4, we get that the fast track students took most or all of the math courses.
