help

OVERVIEW

In an exhibit at Museum of Science in Boston, Massachusetts, balls several inches thick are dropped at regular time intervals into a vertical pegboard bounded by Plexiglas. The balls dribble down, obeying the law of gravity, coming to rest in columns at the bottom. Interestingly the balls do not end up evenly distributed among the columns. Instead, they come to rest in a roughly bell-shaped configuration. This exhibit shows the normal distribution—a concept that ties back to visual displays, studied in Module Two.

The normal distribution is defined as a frequency diagram symmetric about a line through the mean value, which has a shape more or less like a bell (Tapson, 1996). It connects to histograms taught in the second unit of this course because a natural thing for a psychologist to do after creating a histogram is to judge if it has the normal distribution shape. Remember a histogram visually represents measurements taken on a number of people in a psychological study. Although the term “normal” might seem to imply a distribution describing only “normal people’s behavior,” normal in this context is not used in that sense. A normal distribution describes common behavior but also unusual or extremely unusual behavior.

The normal distribution pertains to the analysis of psychological data for a simple reason: In psychology, it is common for a data set placed into a histogram to have a normal shape. As indicated by the definition, the shape need not fit exactly that of a bell to be considered normal. There is a range of shapes one may describe as normal. Statisticians say the normal distribution describes a family of distributions. That is a formal way of stating that, for a distribution to be normal, it can be any of a number of shapes as long as they fit essential criteria (Bennett, Briggs, & Triola, 2014, pp. 161–166; Huck, 2009; “The Normal Distribution,” n.d.).

The normal distribution remains the most widely used statistical model (Ruxton, Wilkinson, & Neuhäuser, 2015). Examples of behaviors described by the normal distribution include self-ratings for mood, anxiety, and energy (Ortiz, Bradler, Garnham, Slaney, & Alda, 2015), scores on a test of emotional intelligence (Martskvishvili, Arutinov, & Mestvirishvili, 2013), scores on a test where individuals describe music in words (Music Description Test;

 

Zimmerman, 1971), and scores on a measure of well-being (Spittlehouse, Vierck, Pearson, & Joyce, 2014).

In a similar way, many physical behaviors follow a normal distribution and objects sometimes record evidence of this distribution. For example, on the carpeting on each step of a staircase in an old home, you may observe a pattern of wear. You see a decreasing amount of wear in either direction from a concentration of wear in the center. In other words, you see a normal distribution imprinted in the carpet. People have tended to walk in the middle but have sometimes strayed from this pattern. At Wells Cathedral in Wells, England, one sees a similar pattern of wear in stone steps of the Prior’s Staircase. As persons have trodden up and down it since the cathedral opened in the year 1239, they have mainly stepped around the middle and less often to the left and right. The stairs have recorded this normal-distribution behavior in a normally distributed pattern of wear.

What does the normal distribution imply about behavior on a deeper level? The normal distribution reflects the operation of chance. If nature had a logo for the operation of chance, nature would use the normal distribution. You can interpret this distribution as a variable in the real world where many factors influenced this variable. A common expression can clarify this statement. When you get very busy, you may say, “I was being pulled in all different directions.” You are expressing that many different demands were placed on you. A variable normally distributed reflects that the variable was recorded in real life, where many other things affected it or pulled it in different directions. When the variable is placed in a diagram, a distribution emerges that looks as if the middle/anchor point was pulled in different directions, resulting in the stretched out display you know as the normal distribution.

A psychologist finds data falling into a normal distribution valuable from a practical standpoint. The properties of the normal distribution and mathematical probabilities associated with any particular region of it located along the horizontal axis (x-axis) are known (Bennett et al., 2014), so a psychologist can determine probabilities associated with any points along the x-axis of the distribution. The significance of this point will make more sense later; here it is simply important to note the normal distribution helps to pave the way for inferential statistics, studied later in this course.

A theme developed throughout this course needs to be sounded once again. Information in this overview and in the textbook allows a psychologist to make careful, educated, informed assessments and decisions about the structure of data. The information does not permit a researcher to go on autopilot. A number of data sets follow a normal distribution. One needs, however, to evaluate every data set case by case. Sometimes distributions emerge besides normal (Swartout, Thompson, Koss, & Su, 2015). Even when a distribution emerges as within the realm of normality, “normal” is a general term; details about how closely (or not) a distribution looks like a perfect bell-shaped curve are still going to help a psychologist understand the data. The tools learned in Module Two—the histogram, the line chart (Bennett et al., 2014), together with knowledge about the normal distribution—permit a student or researcher in every emerging instance of research to tabulate and to express data in a visual display. This analysis might furnish immediate and peremptory answers about structure of data. But perhaps more frequently, this analysis provides direction about which additional analyses to conduct, as well as background knowledge necessary for interpreting results of other procedures.

References

Bennett, J., Briggs, W. L., & Triola, M. F. (2014). Statistical reasoning for everyday life (4th ed.). New York, NY: Pearson.

Huck, S. W. (2009). Statistical misconceptions. New York, NY: Taylor & Francis Group, LLC.

Martskvishvili, K., Arutinov, L., & Mestvirishvili, M. (2013). A psychometric investigation of the Georgian version of the Trait Emotional Intelligence Questionnaire. European Journal of Psychological Assessment, 29(2), 84-88.

Museum of Science, Boston. (2015). Retrieved from http://www.mos.org

The normal distribution. (n.d.). Retrieved from http://www.stat.yale.edu/Courses/1997-98/101/normal.htm

Ortiz, A., Bradler, K., Garnham, J., Slaney, C., & Alda, M. (2015). Nonlinear dynamics of mood regulation in bipolar disorder. Bipolar Disorders, 17,139-149.

Ruxton, G. D., Wilkinson, D. M., & Neuhäuser, M. (2015). Advice on testing the null hypothesis that a sample is drawn from a normal distribution. Animal Behaviour, 107, 249-252.

Spittlehouse, J. K., Vierck, E., Pearson, J. F., & Joyce, P. R. (2014).Temperament and character as determinants of well-being. Comprehensive Psychiatry,55, 1679-1687.

Swartout, K. M., Thompson, M. P., Koss, M. P., & Su, N. (2015). What is the best way to analyze less frequent forms of violence? The case of sexual aggression. Psychology of Violence, 5, 305-313.

Tapson, F. (1996). Barron’s mathematics study dictionary. Hauppauge, NY: Barron’s Educational Series, Inc.

Zimmerman, W. W. (1971). Verbal description of aural musical stimuli. Journal of Research in Music Education. 19(4), 422-432.

4-1 Discussion: What Is Cool About the Normal Distribution?

 

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The normal distribution is notable for its ubiquity and its usefulness. Psychology data sets, when graphed in a histogram, commonly have a normal shape. After reading chapter 5 of your textbook, post something you find interesting about the normal distribution and state why you find it interesting. (RESPONSE SHOULD BE A PARAGRAPH OR TWO)

Complete Milestone Two of the attachments.

PSY 223 Milestone Two Worksheet

Review the critical elements that must be addressed in the final project. Use this worksheet to create Milestone Two.

Points to remember:

· A sample is a subset of a population.

· A random sample is a type of sample. Every element of the population has an equal chance of selection into this kind of sample.

· If a researcher takes a random sample from a population, he or she can conclude the sample mirrors the population. He or she can also conclude research on the sample will be the same as what one would find with the population.

· The larger the size of a sample, the more a sample resembles the population.

· Whether a researcher sampled randomly, and the size of the sample, affects results. Random, large samples provide bases for stronger, more reliable conclusions. Convenience (non-random) samples and/or smaller samples provide the bases for weaker and less reliable conclusions.

· No line divides a “small” sample from a “large” sample. As a guiding start, though, a researcher considers fewer than n = 30 to be relatively small.

· If a researcher divides a sample into groups, this division does not change sample size.

· See Chapter 1 of Statistical Reasoning for Everyday Life for additional information on sampling.

1. Indicate the sample size (n = ), and describe what consequence this sample size will have on your analyses and on your reporting of results.

2. Choose a statistical procedure and explain how the statistical procedure you choose will help to answer your research question. [For this question, refer to the

Choose Your Test document

.]

PSY 223 Choose Your Test

Use this document in Milestone Two to select a statistical procedure for your final project. Beginning in
Module Five, you are going to learn about these in more depth.

Remember: A sample is a population subset. Each data set for the final project includes one or two samples.
Numbers are already assigned to the elements of a sample; so when it says “sample,” you are looking for a
group of measurements.

1. Does the research question for your data set indicate (a) there is one sample and (b) the question
aims to find a relationship between two variables?

You want a correlation. A correlation determines the magnitude and direction of relationship
between two variables (e.g., amount of sleep 30 participants got and length of time they spent on
treadmill next day).

2. Does the research question for your data set indicate (a) there are two groups and (b) the question
aims to find out if the groups differ in some outcome or performance?

You want a two sample t-test. It reveals if the averages of the two groups are “different enough” for
us to conclude that the difference in the way the groups were treated led to the average difference
(e.g. one sample drank coffee, one sample drank decaffeinated coffee, and their average reading
speeds were compared to see if a difference was significant).

3. Does the research question for your data set indicate (a) there is one group and (b) the question

aims to find out if the group’s outcome or performance differed under two conditions?

You want a paired sample t-test. It shows if the average performances of the group under separate
conditions are “different enough” for us to conclude that the different conditions led to the
different performances (e.g. a sample of individuals learn a set of 10 words under influence of an
experimental memory drug; two days later, the same sample learns a set of words without any drug,
and their performances are compared).

PSY 223 Milestone Two Guidelines and Rubric

In this milestone, you will submit Milestone Two Worksheet.

Address the following:

1. Indicate sample size (n = ?), and describe what consequence(s) this sample size will have in terms of analyses and reporting.
2. Using the Choose Your Test document, select a statistical procedure appropriate to your scenario/data. Explain why you selected that test, linking

features of the scenario/data to information from the Choose Your Test document.

This milestone addresses critical elements Section II, parts A and B only. You will do the calculations for Section II parts C, D, E, and F as you complete your final
project.

Specifically, the following critical elements must be addressed:

II. Data Analysis
A. Identify the sample size and explain how it will inform your analysis. In other words, what is the sample size? How will the size of the sample

inform your analysis?
B. Select what statistical procedures should be implemented in your analysis, and justify why you feel these are appropriate.

Guidelines for Submission: Complete the Milestone Two Worksheet using complete sentences. Your worksheet must be submitted as a Microsoft Word
document with double spacing, 12-point Times New Roman font, and one-inch margins.

Critical Elements Proficient (100%) Needs Improvement (75%) Not Evident (0%) Value

Data Analysis: Sample Size Identifies the sample size and explains
how the sample size will inform the
statistical analysis

Identifies the sample size and explains
how the sample size will inform the
statistical analysis but explanation is
cursory or contains inaccuracies

Does not identify the sample size or explain
how the sample size will inform the
statistical analysis

45

Data Analysis: Statistical
Procedures

Selects what procedures should be
implemented in the analysis and justifies
why these statistical procedures are
appropriate

Selects what procedures should be
implemented in the analysis and justifies
why these statistical procedures are
appropriate, but some procedures
selected are not appropriate or the
justification is not logical

Does not select what procedures should be
implemented in the analysis and justify why
these statistical procedures are appropriate

45

Articulation of Response Submission has no major errors related
to citations, grammar, spelling, syntax, or
organization

Submission has major errors related to
citations, grammar, spelling, syntax, or
organization that negatively impact
readability and articulation of main ideas

Submission has critical errors related to
citations, grammar, spelling, syntax, or
organization that prevent understanding of
ideas

10

Total 100%

http://snhu-media.snhu.edu/files/course_repository/undergraduate/psy/psy223/psy223_milestone_two_worksheet

http://snhu-media.snhu.edu/files/course_repository/undergraduate/psy/psy223/psy223_choose_your_test

PSY 223 Final Project Guidelines and Rubric

Overview

The final project for this course is the creation of a statistical analysis report.

The two research courses (PSY 223 and PSY 224) will demystify statistics and research methods in order to show that they are based on simple principles that
apply to situations in the social sciences. In psychology, we need to distinguish what is “real” from what is “not real but looks real.” Is this patient really
depressed? Does this form of group treatment of adolescents work better than a different form of treatment?

In this summative assessment, you will choose a scenario from a given set to be the basis for your statistical analysis report. Within the scenario, you will be
given a data set based on two groups. You will apply the statistical analysis skills you have learned in this course to interpret the data and write up a report of the
results. You will be evaluated not only on your computations but also on your explanation of the interpretation of the data.

The project is divided into three milestones and a final product. The milestones will be submitted at various points throughout the course to scaffold learning
and to ensure quality final submissions. These milestones will be submitted in Modules Two, Four, and Five. The final project will be submitted in Module
Seven.

In this assignment, you will demonstrate your mastery of the following course outcomes:

 Analyze descriptive and inferential statistics for preparing statistically accurate psychological research

 Utilize appropriate statistical techniques for computing descriptive statistics and generating graphs regarding statistical analyses of psychological
research

 Select appropriate statistical procedures for use in statistical analyses regarding psychological research

 Interpret the results of statistical analyses of psychological research data for drawing informed conclusions regarding the implications of psychological
research

 Assess scenarios involving statistical procedures for ensuring alignment with the expectations of the APA Ethical Principles of Psychologists

Prompt

Select a scenario from the Scenarios and Data Sets document to be the basis of your statistical analysis report. When deciding on which scenario to choose,
determine which one is the most beneficial for your area of concentration in psychology. This statistical analysis report will inform stakeholders about the
analysis and interpretation of the data presented in the scenario. Microsoft Excel is the recommended statistical software for this course, and the data sets are
already placed into Excel files. You will first summarize your chosen scenario and discuss ethical issues. You will then begin your data analysis, determining the
appropriate procedures in testing your hypothesis. Finally, you will summarize and interpret your results, making appropriate conclusions based on those results.

Specifically, the following critical elements must be addressed:

I. Introduction
A. Summarize the scenario you have chosen, including participants, the data set presented, and the question that can be answered by the data.
B. Discuss why the scenario exemplifies a study that agrees with APA’s Ethical Principles of Psychologists.
C. Discuss ethical issues that may potentially arise when analyzing and reporting statistical data.
D. Explain what you will do in your data analysis and reporting to ensure alignment with the expectations of APA’s Ethical Principles of

Psychologists.

II. Data Analysis
A. Identify the sample size and explain how it will inform your analysis. In other words, what is the sample size? How will the size of the sample

inform your analysis?
B. Select what statistical procedures should be implemented in your analysis, and justify why you feel these are appropriate.
C. Explain how statistical procedures can help you determine whether the data is attributable to chance factors.
D. Compute the mean and the standard deviation for each set of data using appropriate abbreviations and terminology.
E. Prepare an appropriately labeled histogram for each set of data.
F. Evaluate the shape of each distribution using your created histograms. In other words, what does the shape of each distribution tell us about the

data?

III. Hypothesis
A. Determine whether one mean is higher, showing how you made the determination.
B. Identify the null hypothesis and alternative hypothesis using appropriate statistical symbols and language based on what you are comparing.

IV. Results
A. Based on your results, determine whether the data provide evidence for a valid effect.
B. Explain whether or not the results are statistically significant. Support your response with results from the data analysis.
C. Present properly labeled graphs representing the data analysis results detailed clearly for ease of stakeholder interpretation.

http://snhu-media.snhu.edu/files/course_repository/undergraduate/psy/psy223/psy223_scenarios_and_data_sets

http://snhu-media.snhu.edu/files/course_repository/undergraduate/psy/psy223/psy223_data_sets_excel_files.zip

V. Conclusion
A. Explain your interpretation of the data. In other words, based on your results, what do you think the data mean? What are the potential

implications of this data for the stakeholders? What do these results mean for future research into the topic area?
B. Justify the data analysis procedures you used to reach your interpretation.
C. Discuss whether it would be appropriate to conduct more statistical procedures to further interpret the data.

Milestones
Milestone One: Are Things Okay Ethically?
In Module Two, you will submit the Milestone One Worksheet. In this milestone, you will address the following: (1) Indicate what data set you have chosen and
why. If you chose a particular data set to align with your concentration in psychology, describe in a sentence or two why you have chosen this concentration. (2)
Describe the involved parties in the data set presented and the question that you can answer by the data. (3) Discuss why the data set exemplifies a study that
agrees with the APA’s Ethical Principles of Psychologists. (4) Discuss ethical issues that may arise when analyzing and reporting statistical data. (5) Describe one
way in which you will ensure your reporting of results will align with the APA’s Ethical Principles. This milestone is graded with the Milestone One Rubric.

Milestone Two: What Method Will You Use?
In Module Four, you will submit the Milestone Two Worksheet. In this milestone, you will address the following: (1) Indicate sample size (n = ?) and describe
what consequence(s) this sample size will have in terms of analyses and reporting. (2) Using the Choose Your Test document, select a statistical procedure
appropriate to your scenario/data. Explain why you selected that test, linking features of the scenario/data to information from the Choose Your Test document.
This milestone addresses critical elements Section II, parts A and B only. You will do the calculations for Section II parts C, D, E, and F as you complete your final
project. This milestone is graded with the Milestone Two Rubric.

Milestone Three: Hypothesizing
In Module Five, you will submit the Milestone Three Worksheet. In this milestone, you will indicate the null hypothesis and the alternative hypothesis and state
your understanding of what the hypotheses mean. This milestone is graded with the Milestone Three Rubric.

Final Submission:
In Module Seven, you will submit the statistical analysis report. You will complete the remaining critical elements, Section II C–F (Data Analysis), Section IV
(Results), and Section V (Conclusion). Combine these elements with your revised milestones to develop a complete, polished artifact containing all of the critical
elements of the final project. It should reflect the incorporation of feedback gained throughout the course. This submission will be graded using the Final
Project Rubric.

Deliverables

Milestone Deliverable Module Due Grading

One Are Things All Right Ethically? Two Graded separately; Milestone One Rubric

Two What Method Will You Use? Four Graded separately; Milestone Two Rubric

Three Hypothesizing Five Graded separately; Milestone Three Rubric

Final Submission: Statistical Analysis Report Seven Graded separately; Final Project Rubric

Final Project Rubric

Guidelines for Submission: Your report should be approximately 4 to 5 pages (not including cover page, references, graphs, and/or visuals) and must be written
in APA format. Use double spacing, one-inch margins, and 12-point Times New Roman font. Include a cover page for your report. Include at least three
references, cited in APA format.

Critical Elements Exemplary (100%) Proficient (85%) Needs Improvement (55%) Not Evident (0%) Value

Introduction: Scenario Meets “Proficient” criteria and
summary is exceptionally clear
and contextualized around the
problem or question being
addressed

Provides a summary of the
scenario chosen

Provides a summary of the
scenario chosen, but summary is
cursory or illogical

Does not summarize the scenario
chosen

3

Introduction: Scenario
Agrees

Meets “Proficient” criteria and
uses industry-specific language to
establish expertise

Discusses why the scenario
exemplifies a study that agrees
with the APA’s Ethical Principles
of Psychologists

Discusses why the scenario
exemplifies a study that agrees
with the APA’s Ethical Principles
of Psychologists, but discussion is
cursory or illogical

Does not discuss why the
scenario exemplifies a study that
agrees with the APA’s Ethical
Principles of Psychologists

3

Introduction: Ethical
Issues

Meets “Proficient” criteria and
draws insightful connections
between ethical issues and data
analysis and reporting

Discusses ethical issues that may
potentially arise when analyzing
and reporting statistical data

Discusses ethical issues that may
potentially arise when analyzing
and reporting statistical data, but
discussion is cursory or illogical

Does not discuss ethical issues
that may potentially arise when
analyzing and reporting statistical
data

6.26

Introduction: Ensure
Alignment

Meets “Proficient” criteria and
demonstrates a nuanced
understanding of ethical data
analysis and reporting

Explains what will be done in
personal data analysis and
reporting to ensure alignment
with the expectations of the APA
Ethical Principles of Psychologists

Explains what will be done in
personal data analysis and
reporting to ensure alignment
with the expectations of the APA
Ethical Principles of
Psychologists, but explanation is
illogical or irrelevant

Does not explain what will be
done in personal data analysis
and reporting to ensure
alignment with the expectations
of the APA Ethical Principles of
Psychologists

6.26

Data Analysis: Sample
Size

Meets “Proficient” criteria and
explanation demonstrates a
sophisticated awareness of how
the sample size can inform
statistical analysis

Identifies the sample size and
explains how the sample size will
inform the statistical analysis

Identifies the sample size and
explains how the sample size will
inform the statistical analysis but
explanation is cursory or contains
inaccuracies

Does not identify the sample size
or explain how the sample size
will inform the statistical analysis

6.26

Data Analysis:
Statistical Procedures

Meets “Proficient” criteria and
demonstrates a nuanced
understanding of appropriate
application of statistical
procedures

Selects what procedures should
be implemented in the analysis
and justifies why these statistical
procedures are appropriate

Selects what procedures should
be implemented in the analysis
and justifies why these statistical
procedures are appropriate, but
some procedures selected are
not appropriate or the
justification is not logical

Does not select what procedures
should be implemented in the
analysis and justify why these
statistical procedures are
appropriate

6.26

Data Analysis: Chance
Factors

Meets “Proficient” criteria and
explanation is exceptionally clear
and contextualized

Explains how statistical
procedures can help determine
whether the data is attributable
to chance factors

Explains how statistical
procedures can help determine
whether the data is attributable
to chance factors, but
explanation is illogical

Does not explain how statistical
procedures can help determine
whether the data is attributable
to chance factors

6.26

Data Analysis: Mean
and Standard

Deviation

Computes the mean and
standard deviation accurately for
each set of scores using
appropriate abbreviations and
terminology

Computes the mean and
standard deviation for each set of
scores, but computations are not
accurate or do not use
appropriate abbreviations and
terminology

Does not compute the mean and
standard deviation for each set of
scores

6.26

Data Analysis:
Histogram

Prepares an accurate,
appropriately labeled histogram
graph for each set of scores or
score distribution

Prepares a histogram graph for
each set of scores or score
distribution, but the graphs are
not accurate or are not
appropriately labeled

Does not prepare a histogram
graph for each set of scores or
score distribution

6.26

Data Analysis: Shape Meets “Proficient” criteria and
evaluation demonstrates keen
insight into what the shape of a
distribution says about the data

Evaluates the shape of each
distribution using created
histograms

Evaluates the shape of each
distribution using created
histograms, but evaluation is
cursory or contains inaccuracies

Does not evaluate the shape of
each distribution using created
histograms

6.26

Hypothesis: Whether
One Mean is Higher

Accurately determines whether
one mean is higher, showing how
the determination was made

Determines whether one mean is
higher, but result is inaccurate or
does not show how the
determination was made

Does not determine whether one
mean is higher

6.26

Hypothesis: Null
Hypothesis and

Alternative Hypothesis

Accurately identifies the null
hypothesis and alternative
hypothesis in language based on
what is being compared and
using appropriate statistical
symbols

Identifies the null hypothesis and
alternative hypothesis in
language based on what is being
compared, but identification is
not accurate or does not use
appropriate statistical symbols

Does not identify the null
hypothesis and alternative
hypothesis

6.26

Results: Valid

Accurately determines if the data
provides evidence for a valid
effect

Determines if the data provides
evidence for a valid effect, but
the determination is illogical or
inaccurate

Does not determine if the data
provides evidence for a valid
effect

3.76

Results: Statistically
Significant

Meets “Proficient” criteria and
explanation is exceptionally clear
and contextualized

Explains whether or not the
results are statistically significant

Explains whether or not the
results are statistically significant,
but explanation is cursory or
illogical

Does not explain whether or not
the results are statistically
significant

3.76

Results: Graphs

Meets “Proficient” criteria and
graphs are exceptionally well
developed and readable

Presents accurate, properly
labeled graphs representing the
data analysis results detailed
clearly for ease of stakeholder
interpretation

Presents graphs representing the
data analysis results, but the
graphs are inaccurate,
improperly labeled, or are lacking
in detail

Does not present graphs
representing the data analysis
results

6.26

Conclusion:
Interpretation

Meets “Proficient” criteria and
uses discipline-specific
terminology to establish
expertise without overwhelming
stakeholders

Explains the interpretation of the
data

Explains the interpretation of the
data, but explanation is cursory
or illogical

Does not explain the
interpretation of the data

3.76

Conclusion: Data
Analysis Procedures

Meets “Proficient” criteria and
demonstrates a deep
understanding of ethical data
analysis procedures

Justifies the data analysis
procedures used to reach the
interpretation

Justifies the data analysis
procedures used to reach the
interpretation, but justification is
illogical

Does not justify the data analysis
procedures used to reach the
interpretation

3.76

Conclusion: More
Statistical Procedures

Meets “Proficient” criteria and
discussion is exceptionally clear
and contextualized

Discusses whether it would be
appropriate to conduct more
statistical procedures to further
interpret the data

Discusses whether it would be
appropriate to conduct more
statistical procedures to further
interpret the data, but discussion
is cursory or contains issues of
clarity

Does not discuss whether it
would be appropriate to conduct
more statistical procedures to
further interpret the data

3.76

Articulation of
Response

Submission is free of errors
related to citations, grammar,
spelling, syntax, and organization
and is presented in a professional
and easy to read format

Submission has no major errors
related to citations, grammar,
spelling, syntax, or organization

Submission has major errors
related to citations, grammar,
spelling, syntax, or organization
that negatively impact readability
and articulation of main ideas

Submission has critical errors
related to citations, grammar,
spelling, syntax, or organization
that prevent understanding of
ideas

6.34

Total 100%