When the four areas (critical components) identified in the graphic are aligned, they work together to
ensure students achieve the desired learning outcomes.
Hit Course Goals and this text pops up:
Course goals are broad statements of intent that are not necessarily
measurable but provide the foundation from which specific learning objectives are developed and
aligned.
EX Course Goal: To develop students’ basic competencies in specifying linkages among research
questions, method, and inferences.
Learning objectives are measurable steps that lead toward a goal.
Objectives should be written in statements that identify what students should KNOW, UNDERSTAND, and
BE ABLE TO DO as a result of participating in planned instructional activities. The learning objectives
indicate and direct appropriate assessment methods, frame what and how content is taught, guide class
activities, help identify resources to support student learning, and provide the basis for program faculty
discussions on courses and program review.
EX: As a result of the course, students will:
KNOW
the four scales of measurement
different kinds of graphs and their proper use (and misuses)
applications of descriptive statistic, including central tendency, variability, correlation
the purpose of linear regression
the types of random sampling and the purpose of each
UNDERSTAND
Statistics is a useful language for symbolically modeling quantitative data and thus simplifying and
analyzing our world
Data consists of structure plus variability
Statistics can be used to make valuable, reliable inferences from quantitative data
The appropriate communication and interpretation of statistics is essential to avoid statistical abuse
and/or misunderstanding
BE ABLE TO DO
*Engage in statistical problem solving
*To randomly select a sample from a population
*Perform the following procedures using statistical software
Recode existing variables in a dataset and generate new variables from existing variables
Produce descriptive statistics including frequency distributions, measures of central tendency,
measures of variability, and graphs such as histograms, box plots, and scatterplots
Perform a test of statistical significance to assess the relationship between two quantitative
variables and interpret and communicate the results in writing
Construct a prediction model using simple linear regression and interpret the resulting values.
Choose and apply appropriate inferential analyses to real situations in order to draw conclusions
about a population.
Recognize the strengths and limitations of quantitative data analysis and quantitative research
methods
*Communicate using scholarly language (i.e., APA writing guidelines) the analyses, the results of the
analyses, and the interpretation and conclusion of the analyses.
Student outcomes are the specific observable and measureable skills, attitudes,
and/or knowledge that students demonstrate that provide evidence that intended learning has occurred
as a result of the instructional activities.
EX: How Measured
BACKGROUND NOTE: Two exams are given to assess students’ obtainment of the learning objectives
in a de-contextualized manner. The project is intended to provide students the opportunity to engage
in a real-life situation of asking a research question, designing a study to address that research
question, and carrying out the study. The project is the culmination of the semester long instructional
cycle.
I.
Exam 1: exam 1 will focus on the following learning objectives identified below.
Know:
the four scales of measurement
different kinds of graphs and their proper use (and misuses)
applications of descriptive statistic, including central tendency, variability, correlation
APA writing guidelines for reporting statistical analyses
Be Able To Do:
*Perform the following statistical procedures using statistical software
Recode existing variables in a dataset and generate new variables from existing variables
Produce descriptive statistics including frequency distributions, measures of central tendency,
measures of variability, Pearson’s correlation and graphs such as histograms, box plots, and
scatterplots
Interpret the descriptive analyses (above) resulting values and draw conclusions using APA
writing guidelines
II.
Exam 2: exam 2 will focus on the following learning objectives:
Be Able To Do:
*construct a prediction model using simple linear regression and interpret the resulting values
and communicate in scholarly language the meaning of the resulting model.
*perform a test of statistical significance to assess the relationship between two quantitative
variables and interpret and communicate the results in writing.
III.
On-going Course Project
There will be a project for the course that will last the entire length of the course. The project is
a way for you to put to use all of the ideas covered in the course to study a topic of your
interest. You will secure data, generate hypotheses, analyze the data, and draw conclusions
using the tools and procedures that are presented in class. There will be deadlines for each part
of the project. However, the final component of project will be due <DATE>. The outline below
describes each of the components of the project:
1. Decide on a topic that is of interest to you that has some significance and
requires quantitative data (nominal through interval/ratio). Due by:
<DATE> If you do not have access to a dataset, please inform the
instructor.
2. DUE <DATE>: Collect data that are suitable for descriptive statistics and
hypotheses testing.
3. DUE <DATE>: Central Tendency & Variability; Write-up
4. DUE <DATE>: Correlation & Regression; Write- up
5. DUE <DATE>: Hypothesis Testing, including research question, Regression
Analysis; Write-up
Show Project Rubric
Your project paper should include:
a. An introduction to each question that you are trying to address.
b. An explanation, in detail, of what you did to answer your question (including a description of
your data) and why you chose this method.
c. A presentation of your data using a variety of appropriate methods; charts/graphs/tables (all
done following scholarly formatting)
c. All the statistical analyses necessary to answer your question. This includes checking
conditions for inference.
e. A conclusion and a contextualized interpretation based on the results.
f. Limitations (Include a discussion on how your project could be improved.)
(Note: Over the course of the semester you will have several different types of analyses and as
we move into the inferential component of the course, you may need to go back and revisit
some of your conclusions based on more sophisticated analyses.
You will be evaluated based on the soundness of your statistical thinking, the correctness of
your statistical procedures, the presentation of your statistical procedures, and the professional
presentation of your work.
Stat Project Rubric – Analytic Rubric Example
Domain
Proficient
Overview of Analysis
Provided explanation of
research question(s) and
real world context of
results.
Named and defined
variables clearly.
Definition of Variables
Frequency Distribution
Tables and Histograms
Descriptive Statistics
Research variables
named; measurement
and possible values of
each variable clearly
defined.
Each research variable is
represented with
appropriate, clearly
labeled, charts and
tables to support the
narrative explanation
(see Descriptive
Statistics)
Each research variable is
fully and appropriately
described.
Level of Proficiency
Developing
Stated research
questions but did not
explain real world
context.
Named variables and
provided some
explanation but more is
needed to fully
understand the data.
Research variables
names, but details are
lacking on how variables
are measured or
quantified.
Charts and tables are
present for most of the
research variables but
missing labels or have
inappropriate labeling.
Some misinterpretation
of the charts and/or
tables in the narrative
explanation (See
Descriptive Statistics)
Research variables are
described but there are
minor
misinterpretations.
Comments
Novice
Did not state research
questions and did not
make connection to real
world context.
No indication of
variables or explanation
of the data.
Research variables are
not defined, defined
inappropriately or
inadequately.
Charts and tables
missing or inappropriate
presentation of data
given the research
variables.
No narrative to
accompany the
charts/tables (See
Descriptive Statistics).
Missing or inaccurate
description of each
research variable.
Pearson’s Correlation
Coefficient
Scatter Plot
Linear Regression
Confidence Intervals
The correlation
coefficient and its
interpretation (e.g., not
causation) are correctly
reported,
Scatter plot is present
and correct; wellillustrated, scaled, and
readable, axes are
labeled, and there is a
discussion of
connections to the
correlation coefficient, r
Regression line and
equation are both
correct; slope is
reported and
interpreted accurately;
R2 is reported and
interpreted accurately.
A prediction example
relevant to the
identified context is
used to demonstrate
the regression equation.
The correlation
coefficient is reported
but it is misinterpreted
(e.g., linked to
causation,
Scatter plot is present
and correct but there
are issues with the
illustration, scalability,
and/or labeling. There is
not a discussion of the
connection to the
correlation coefficient, r
Regression line is
present and correct;
regression equation is
reported and is correct;
slope is reported
correctly and the
interpretation is
partially correct. R2 is
reported correctly and
interpretation is
partially correct.
Confidence interval
presented with correct
interpretation to
Confidence interval
presented but missing
interpretation or
Correlation coefficient is
not reported or
incorrect; incorrect or
inadequate explanation
of the
Scatter plot missing, or
incorrect (e.g.,
confusion between
predictor and criterion
variables).
No connection to the
correlation coefficient, r
Regression line is
missing, incorrect, or
poorly illustrated;
equation of regression
line not reported and
inadequately explained
or not explained at all.
Slope of regression line
is not reported correctly
and/or the
interpretation of the
slope is incorrect.
R2 is completely missing
from Discussion.
Missing confidence
interval or explanation
missing or inappropriate
context
Testing of Means
Hypothesis Testing
Procedure
Discussion
inappropriate
is provided.
interpretation provided.
Appropriate t-test
Appropriate t-test
Inappropriate t-test
implemented to address implemented to address implemented and/or no
stated hypotheses;
stated hypotheses;
consideration of
consideration of
incomplete
statistical assumptions.
statistical assumptions
consideration of
provided.
statistical assumptions
provided.
Null and alternative
Null and/or alternative
Hypotheses are missing
hypotheses are clearly
hypotheses are stated
or are inappropriately
and correctly stated
with some inappropriate
stated (incorrect
using appropriate
use of statistical
symbols).
statistical symbols.
symbols.
Incorrect statistical
An appropriate
An appropriate
procedure is
statistical test is
statistical test is
implemented to address
implemented correctly.
implemented but
the hypotheses.
Correct conclusions are
conclusions drawn
Incorrect conclusions
drawn based upon the
based upon obtained
are drawn based upon
obtained critical value.
critical value are
the obtained critical
Interpretation of the
incorrect.
value or conclusions are
conclusion relative to
Misinterpretation of the
completely missing.
the hypotheses in
conclusions and/or
Misinterpretation of the
clearly stated and
lacking contextualization
conclusion relative to
accurate within the
and connection back to the hypotheses with no
given context. No
the hypotheses.
contextualization or
unwarranted
connection of the stated
conclusions.
hypotheses.
Discussion of results is
Adequate discussion of
Implications of results
insightful; adds meaning
results but limited
not discussed; no
and significance to the
reflection into why the
attempt to explain
Presentation
findings.
results occurred.
findings. Report draws
unwarranted
conclusions or uses
inappropriately certain
language (e.g., “we
proved”).
The document is written
with logic, clarity, and
precision using scholarly
language. The text is
logical and coherent.
There is consistent
application of the
scholarly writing and
publication guidelines.
The document tends to
be logically organized
but there is a lack of
precision and clarity.
The use of scholarly
language is sporadic
throughout the
document. Inconsistent
or inappropriate
application of scholarly
writing and publication
guidelines.
The document is not
written in a scholarly
fashion or lacks
precision and clarity.
Failure to follow
scholarly writing and
publication guidelines.