6210 Week 6 Assignment 2 How to Critique A Journal Article
1. Select a quantitative article from a peer-reviewed journal that reports on research that
uses a t-test for statistical analysis. Remember, there are 3t-tests: independent
samples, one sample, and paired samples.
Your article must report on 1 of these tests.
Do not select an article that uses multiple regression, ANOVA, multivariate
analysis, correlation, etc.
2. Write a critique:
State if the researchers chose the correct t-test and explain why or why not the
choice is either correct or incorrect. Hint: if you believe the t-test choice is
incorrect, find another article.
Explain why the researchers chose a t test. Do this by examining the RQ, null
hypothesis, IV and DV:
The RQ should address differences in the DV based on the IV.
The IV must be nominal with only 2 groups: for example, male or female.
The DV should be interval or ratio.
3. Discuss the data display (datasets, charts, graphs, etc.): Hint; if the display requires a
written explanation it should not be in the article.
4. Discuss if the data stand alone:
Review the results section for statements that reject or fail to reject the null
hypothesis and/or state statistical significance has or has not been achieved.
If the statement(s) are supported by the statistics, then the data 'stand
alone.' If the statement(s) are not supported by the statistics then the data do
not 'stand alone.'
The t Test for Related Samples
The t Test for Related Samples
Program Transcript
MATT JONES: As its name implies, the independent samples t-test has the
assumption of the independence of observations. But that's not always the case.
Sometimes we take multiple observations of the same unit of analysis, such as a
person, over time. In this case, we'll use a paired sample t-test, sometimes
referred to as the dependent sample t-test. Let's go to SPSS to see how we do
this.
To perform the paired sample t-test in SPSS, we once again go to Analyze,
Compare Means, and down to the Paired Sample T-test. SPSS doesn't require
much information here;; only the pair of variables of which we would like to test.
We have a simulated data set here for statistical anxiety of students. Students
were provided with an instrument that measures their anxiety around statistical
topics on a number of different constructs-- teachers, interpretation, asking for
help, worth, and self-conceptualization.
They were given the test at the beginning of the class and at the conclusion of a
class. Hence, why in the value labels we see pre-test and post-test. As a teacher,
I might have some interest in determining whether students felt more comfortable
with me or had lowering anxiety over time. This is perfect for a paired sample t-
test. To perform this paired sample t-test, we'll go to Analyze, Compare Means,
the Paired Sample T-test.
SPSS doesn't ask for much information;; only the pair of variables of which I
would like to test. In this case, teacher pre-test and teacher post-test. So this is a
classic before and after. The first piece of output I obtain from the paired sample
t-test are some descriptive statistics, specifically around the pairwise comparison
I'm looking at, which is the teacher subscale pre-test and post-test.
I see that there is mean on the pre-test of 17.32 and on the post-test, an 18.44.
So it appears, at least from a descriptive sense, that there is a higher mean on
the post-test than the pre-test. On the instrument, higher scores on an item or the
subscale indicate higher levels of anxiety for that specific attitude. Except for this
specific subscale, fear of statistics teachers, where higher scores actually
indicate lower levels of anxiety.
So if post scores are higher than pre scores, that means on average, students
feel lower levels of anxiety and more positive attitude about their statistics
teacher. I can see here, at least from a descriptive sense, that that appears to be
the case. But from the sample, I am performing a test of statistical significance.
Next to the mean, I'm provided with the sample size 25-- 25 observations pre-test
and 25 observations post-test, all the same person-- the standard deviation for
the pre-test and the post-test, and the standard error of the mean.
©2016 Laureate Education, Inc.
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The t Test for Related Samples
Next, let's go down and interpret the paired sample test itself. We can see that on
average, there was a difference of 1.12 units on the scale with a standard
deviation of 2.50. From the 95% confidence interval, we see that the true
difference is somewhere between 2.15 and 0.085. We have a t-statistic of 2.235
and an associated p-value of 0.035.
At the 0.05 level, the results are statistically significant and we can say that there
is a significant difference between pre-test scores and post-test scores.
Therefore, we can reject the null hypothesis that there is no difference. On
average, it appears on the post-test, students had lower levels of anxiety about
their statistics teacher.
This last example illustrated that students felt more comfortable with statistics as
time progressed and specifically felt less anxious about their statistics instructor. I
certainly hope this example rings true for you, and that you feel comfortable or at
least don't self-identify as being anxious about statistics at the conclusion of this
course. I encourage you to review your textbook, review the videos, ask your
instructor for help, and also research the resources here available at Walden
University to help you succeed.
©2016 Laureate Education, Inc.
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The t Test for Independent Samples
The t Test for Independent Samples
Program Transcript
MATT JONES: The independent samples t-test is a comparison of means test
that compares two means across an independent categorical variable. Let's go to
SPSS to see how we conduct this procedure. In my independent sample t-test, I
would like to test for any possible differences between socioeconomic status and
respondent's race. In SPSS in my Variable View for the race variable, with the
label "What is respondent's race of first mention?," I can click on the values. The
reason I'm doing this is because an independent sample t-test can only test for
differences in two means at one time. Therefore, I can only choose two races for
this test. I can see in the race variable that there are a number of races present
within this variable.
For this test, I will choose respondents that self-identified as white, which is
denoted as 1, and respondents to self-identified as black or African American,
denoted as 2. I'll need to remember those for the next procedure. To perform this
procedure, Analyze, Compare Means, independent sample t-test. My test
variable is my dependent variable for the variable of which a mean is calculated
on. Therefore, it is that metric level variable, or any other variable where it makes
sense to calculate a mean. In this case, it's the socioeconomic status index of the
respondent.
Click on that. Move it over to the test variable. The grouping variable is my
categorical variable. And in this case, it is the respondent's race. I move
respondents race over to the grouping variable. Right away, behind the variable
name, you will see a set of parentheses with two question marks. This is SPSS's
prompt to tell me what races I should enter. SPSS knows that it can only
calculate two means and therefore is asking me to define groups. So I must click
on the define groups, group 1 and group 2. For group 1, I'm going to enter the
value number of 1, which were those respondents that self-identified as white.
For group 2, I'm going to enter 2, which for those respondents that self-identified
as black or African American. Click Continue, and once I click OK, I will receive
the output for my independent sample t-test.
The first piece of output I'm provided are the group statistics. I could look at the N
and get an idea of the sample that ended up in the test. There are 1,094 white
respondents and 191 black or African American respondents. I can see from the
descriptive statistics that the mean socioeconomic status index score for whites
is 50.99, and for black or African Americans it's 44.96. I'm also provided with
standard deviations for each mean, as well as the standard error of the mean.
Before I interpret the independent sample t-test, I must first examine the
Levene's test for equality of variances. An assumption of the independent
samples t-test is that variances are equal across the two groups. SPSS, by
default, provides you with this test to test for equality of variances. There's an F
©2016 Laureate Education, Inc.
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The t Test for Independent Samples
statistic, an associated p value with it. The Levene's test tests the null hypothesis
that variances are equal. As you can see, the p value is 0.059, which is slightly
above the conventional 0.050 threshold. In this case, you have to make a
decision whether you reject or retain this null. If you set your level of significance
at 0.050, and since 0.0059 is slightly above that, you would fail to reject the null,
and assume equal variances.
Another option you might have is since this is so close to being statistically
significant, you could also assume unequal variances, especially since you have
an imbalance in the sample size above. For this specific test, I'm going to choose
to interpret equal variances not assumed. As such, I interpret the bottom row.
Here, I have a t statistic of 4.216, an associated p value of 0.000, which means
the results are statistically significant at the 0.001 level. The mean difference
between white and black or African Americans, on average, is 6.02, with a 95%
confidence interval of the difference being between 3.21 and 8.84. Therefore, I
can safely reject the null hypothesis and conclude that there is a significant
difference in socioeconomic status between those who identify as white and
those who identify as black or African American.
©2016 Laureate Education, Inc.
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