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Required Unit Resources
In order to access the following resources, click the links below:
Badii, M. (2009, November 23). ANOVA with Excel [Video]. YouTube.
https://www.youtube.com/watch?v=leHOBf_-9kM
A transcript of this video is available.
Blake, K. (2011, April 9). Performing an independent samples t test in Excel [Video]. YouTube.
https://www.youtube.com/watch?v=norKDF0MH0M
A transcript of this video is available.
Brewer, E. W., & Kubn, J. (2010). Causal-comparative design. In N. J. Salkind (Ed.), Encyclopedia of
research design (pp. 125-131). SAGE.
http://methods.sagepub.com.libraryresources.columbiasouthern.edu/reference/encyc-of-researchdesign/n42.xml
Glen, S. (2013, December 21). How to do a two sample t test paired two sample for means in Excel 2013
[Video]. YouTube. https://www.youtube.com/watch?v=RHBIQ2reACM&t=8s
A transcript of this video is available.
UNIT VI STUDY GUIDE
Data Analysis: t Test and ANOVA
MBA 5652, Research Methods 2
UNIT x STUDY GUIDE
Title
Grange, J. (2011, April 7). t-test in Microsoft Excel [Video]. YouTube.
https://www.youtube.com/watch?v=BlS11D2VL_U
A transcript of this video is available.
Unit Lesson
Data Analysis of t Test and ANOVA
Unit V focused on the use of parametric statistical procedures, correlation analysis and regression analysis, to
test hypotheses. Unit VI focuses on two additional parametric statistical procedures used to test hypotheses.
Those two additional procedures are the t test and ANOVA. Like correlation analysis and regression analysis,
the t test and ANOVA are forms of inferential statistics. Predictions are stated in the form of hypotheses,
sample data are collected and tested, and statistically significant results are used to make inferences about
the population of interest (Zikmund et al., 2013).
As was pointed out several times throughout the course, hypothesis testing either looks for statistically
significant relationships between variables or statistically significant differences between variables or groups.
While correlation analysis and regression analysis look for relationships between variables, the t test and
ANOVA look for differences between variables or groups. Consider these examples of research questions
that may be answered using t tests and ANOVA.
• Are there differences in air quality between sites A, B, C, and D?
• Are there differences in employee safety training scores before and after completion of a training
course?
• Are there differences in box weight for cereal coming off production Line 1, Line 2, Line 3, and Line
4?
• Are there differences in product satisfaction between male and female consumers?
On the surface, it would seem these questions could easily be answered by simply comparing means. If
samples show that the mean weights of cereal boxes coming off production Line 1, Line 2, Line 3, and Line 4
are 18.2 oz., 18.4 oz., 17.8 oz., and 18 oz. respectively, it would appear the answer is “yes,” there are
differences in box weights for products coming off Line 1, Line 2, Line 3, and Line 4. There is, however, one
important point that prevents drawing any hasty conclusions about mean differences between variables or
groups; mean averages must be tested to determine if statistically significant differences exist. Statistical
procedures, like the t test and ANOVA, are interested in the mean, but they also analyze how the data points
are dispersed around the means. The means from two samples may appear different, but because of the
variance of each data set, there may, in fact, be no statistically significant differences in means.
The t test is used to compare two means (e.g., product satisfaction between male and female consumers)
while ANOVA is used to compare more than two means (e.g., air quality between sites A, B, C, and D).
Experimental and quasi-experimental research designs often use t tests and ANOVA. These are extremely
powerful and useful procedures since variables can be manipulated and controlled to make claims of
causation. For this reason, these statistical procedures are the primary tools used to determine the efficacy of
drugs. This could be seen in testing for the efficacy of a new cancer drug. For example, a control group will
receive a placebo (independent variable1 [IV1]) while an experimental group receives a new drug (IV2). After
the IV1 and IV2 are administered, the control group’s tumor size (dependent variable [DV]) will be compared to
the experimental group’s tumor size. If the mean size of experimental group’s tumor is less than the mean
size of the control group’s tumor and the differences are statistically significant, a claim can be made that the
new drug (IV2) caused a reduction in tumor size (assuming all other variables were held constant).
In many business and social science settings, it is impractical, impossible, unethical, or cost-prohibitive to use
experimental research designs. As an alternative, researchers use causal-comparative research designs (i.e.,
ex post facto designs) to similarly look for differences between groups by comparing means. Since causal comparative designs are ex post facto, meaning events and variables are analyzed after the fact, it is not
possible to manipulate and control variables. Since causal-comparative designs cannot control variables, as
do experimental designs, causation cannot be claimed (even though the name causal-comparative would
MBA 5652, Research Methods 3
UNIT x STUDY GUIDE
Title
suggest otherwise). Nevertheless, causal-comparative designs are effective and frequently use the t test and
ANOVA in business and social science research because they can make strong inferences about the effect
the IV has on the DV (Brewer & Kubn, 2010).

Type of Service: Academic paper Writing
Type of Assignment: Research paper
Subject: Business
Pages/words: 2/550
Academic level: Undergraduate
Paper format: APA
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Language style: US English