Rubic_Print_FormatCourse CodeClass CodeADM-560ADM-560-O500Your Per.docx

Rubic_Print_FormatCourse CodeClass CodeADM-560ADM-
560-O500Your Personal
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Satisfactory (74.00%)Satisfactory (79.00%)Good
(87.00%)Excellent (100.00%)CommentsPoints
EarnedContent70.0%Explain how student would approach the
situation based on personal values
assessment.35.0%Explanation of how student would approach
the situation based on personal values assessment is
missing.Explanation of how student would approach the
situation based on personal values assessment is vague and
inconsistent.Explanation of how student would approach the
situation based on personal values assessment is
present.Explanation of how student would approach the
situation based on personal values assessment is present and
clear.Explanation of how student would approach the situation
based on personal values assessment is clear and
concise.Describe how student's personal values inform and
instruct personal power35.0%Description of how student's
personal values inform and instruct student's personal power is
missing.Description of how student's personal values inform
and instruct student's personal power is vague and
inconsistent.Description of how student's personal values
inform and instruct student's personal power is present and
makes some connection to research.Description of how student's
personal values inform and instruct student's personal power is
present, clear, and makes connections to research.Description of
how student's personal values inform and instruct student's
personal power is clear, concise, and makes connections to
research.Organization and Effectiveness20.0%Thesis
Development and Purpose7.0%Paper lacks any discernible
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appropriate to purpose.Thesis is clear and forecasts the

development of the paper. Thesis is descriptive and reflective of
the arguments and appropriate to the purpose.Thesis is
comprehensive and contains the essence of the paper. Thesis
statement makes the purpose of the paper clear.Argument Logic
and Construction8.0%Statement of purpose is not justified by
the conclusion. The conclusion does not support the claim
made. Argument is incoherent and uses noncredible
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lacks consistent unity. There are obvious flaws in the logic.
Some sources have questionable credibility.Argument is
orderly, but may have a few inconsistencies. The argument
presents minimal justification of claims. Argument logically,
but not thoroughly, supports the purpose. Sources used are
credible. Introduction and conclusion bracket the
thesis.Argument shows logical progressions. Techniques of
argumentation are evident. There is a smooth progression of
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authoritative.Clear and convincing argument that presents a
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sources are authoritative.Mechanics of Writing (includes
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used.Frequent and repetitive mechanical errors distract the
reader. Inconsistencies in language choice (register) or word
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varied.Some mechanical errors or typos are present, but they are
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template is used, but some elements are missing or mistaken. A
lack of control with formatting is apparent.Appropriate template
is used. Formatting is correct, although some minor errors may
be present. Appropriate template is fully used. There are
virtually no errors in formatting style.All format elements are
correct. Documentation of Sources (citations, footnotes,
references, bibliography, etc., as appropriate to assignment and
style)5.0%Sources are not documented.Documentation of
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although some formatting errors may be present.Sources are
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is mostly correct.Sources are completely and correctly
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is free of error.Total Weightage100%
Alternative hsbdataB.sav
Chapter Seven Data.sav
college student data.sav
DataFemales.sav
DataMales.sav
hsbdata.sav
Chapter6/Chapter Guides.pdf
IBM SPSS for Introductory Statistics: Use and Interpretation,

5th Ed. (Morgan, Leech, Gloeckner & Barrett) Instructor's
Manual by Gene W.
Gloeckner and Don Quick
Chapter 6 – Selecting and Interpreting Inferential Statistics
Study Guide
OBJECTIVES:
The student will be able to:
1. Identify the general design classification for difference
research questions.
2. Explain the distinctions of within subjects design versus
between groups design
classifications.
3. Utilize a decision tree (Figure 6.1) to guide the selection of
appropriate inferential
statistics (Tables 6.1-6.4).
a. Identify the research problem.
b. Identify the variables and their level of measurement.
c. Select appropriate inferential statistic.
4. Describe the relationship between difference and
associational inferential statistics as a
function of the general linear model.
5. Interpret the results of a statistical test.
a. Determine whether to reject the null hypothesis.
b. Determine the direction of the effect.
c. Evaluate the size of the effect.

6. Discuss the relationship between statistical significance and
practical significance.
TERMINOLOGY:
• variables
• levels of measurement
• descriptive statistics
• inferential statistics
o difference inferential statistics
o associational inferential statistics
• difference question designs
• between group designs
• within subjects design (repeated measures design)
• single factor designs
• between groups factorial designs
• mixed factorial designs
• basic (bivariate) statistics
o phi or Cramer’s V
o eta
o Pearson product moment correlation
o Kendall’s tau or Spearman rho
• complex statistics
o factorial ANOVA
o multiple regression
o discriminant analysis
o logistic regression

Manual by Gene W.
o MANOVA
o ANCOVA
• loglinear
• general linear model
• statistical significance
o critical value
o calculated value
o statistically significant
o Sig.
• practical significance
• effect size
o r family of effect size measures
o d family of effect size measures
• confidence intervals
ASSIGNMENTS: See additional activities and extra SPSS
problems for assignment examples.
Chapter6/Chapter Outlines.pdf
Manual by
Gene W. Gloeckner and Don Quick

Chapter 6 – Selecting and Interpreting Statistics
Chapter Outline
I. General Design Classifications for Difference Questions
A. Labeling difference question designs.
1. State overall type of design (e.g. between groups, within
subjects).
2. State the number of independent variables.
3. State the number of levels within each independent variable.
B. Between groups designs: each participant in the research is
in only one
condition or group.
C. Within subjects or repeated measures designs
1. Within subjects designs.
a. Each participant in the research receives or experiences all
of the conditions or levels of the independent variable.
b. Also includes designs where participants are matched (e.g.
parent & child; husband & wife).
2. Repeated measures designs: each participant is assessed more
than once (e.g. pretest & posttest).
D. Single factor (one-way) design
1. Has only one independent variable.
2. Factor and way are other terms for group difference
independent
variables.
E. Between groups factorial design

1. When there is more than one group difference independent
variable.
2. Each level of one factor (independent variable) is possible in
combination with each level of the other factor(s).
a. The number of levels of each factor is used in the
description of the design.
b. For example: a design that includes gender (2 levels) and
ethnicity (4 levels) would be labeled as a 2 x 3 between
groups factorial design.
F. Mixed factorial design: Has both a between groups
independent variable
and a within subjects independent variable.
G. Describing designs
1. Each independent variable is described using one number that
represents the number of levels for that variable.
2. Example: 3 x 4 between groups factorial design would have 2
independent variables, one with 3 levels and one with 4 levels.
II. Selection of Inferential Statistics
A. Types of research questions.
1. Difference questions: compare groups and utilize difference
inferential statistics. (Tables 6.1 & 6.3)
a. Basic (bivariate) statistics: one independent and one
dependent variable.

Manual by
b. Complex statistics: three or more variables.
2. Associational questions: examine the association or
relationship
between two or more variables and utilize associational
inferential statistics (Tables 6.2 & 6.4).
B. Using Tables 6.1 and 6.4 to Select Inferential Statistics
1. Decide the number of variables.
a. 2 variables = Tables 6.1 or 6.2
b. 3 or more variables = Tables 6.3, 6.4 or 6.5
(Basic 2 variable Questions and Statistics)
2. If there are two variables and the independent variable is
nominal
or has 2-4 levels = Table 6.1.
a. Identify number of levels of IV.
b. Identify type of research design (between or within).
c. Determine the type of measurement for the DV.
3. If there are 2 variables and both are nominal use the bottom
rows
of Table 6.1 (difference question) or Table 6.2 (associational
question).
4. If there are 2 variables and both variables have 5 or more
ordered
levels use Table 6.2 (associational question).

(Complex Questions and Statistics-3 or more variables)
5. If there is one normal/scale DV and the IV’s (2 or more) are
nominal or have a few ordered levels use Table 6.3.
6. If there is one normal/scale DV and the IV’s/predictors (2 or
more) are normal/scale or dichotomous use the top row of Table
6.4 (complex associational question).
7. If there is one DV that is nominal or dichotomous and there
are 2
or more IV’s use the bottom row of Table 6.4 (or 6.3).
8. If there are 2 are more normal (scale) DV’s use the general
linear
model to do MANOVA.
III. The General Linear Model (GLM)
A. Difference between associational and difference questions.
1. Mathematically, the distinction between associational and
difference questions is artificial.
2. Both associational and difference inferential statistics serve
the
purpose of exploring and describing relationships (Fig. 6.2).
a. The GLM subsumes both associational and difference
inferential statistics.
b. The relationship between the IV and DV can be expressed
by an equation with weights for each of the
independent/predictor variables plus an error term.
IV. Interpreting the Results of a Statistical Test

A. Statistical Significance
1. The SPSS calculated value is compared to a critical value
found
in a statistics table.
2. Statistically significant: probability (p) is less than the preset
alpha (usually .05).
Manual by
a. Sig.: SPSS label for the p value.
b. Usually, if the calculated value (t, F, etc.) is large, the
probability (p) is small.
c. This Sig. is also the probability of committing a Type I
error (rejecting the null hypothesis when it is actually true).
3. The p and the null hypothesis
a. p > .05: don’t reject the null hypothesis; results are not
statistically significant and could be due to chance.
b. p < .05: reject the null hypothesis; results are statistically
significant and are not likely due to chance.
B. Practical Significance versus Statistical Significance
1. Statistical significance does not necessarily insure that the
results
have practical significance or are important.

2. Effect size and/or confidence intervals must be examined to
determine the strength of association.
a. It is possible, with a large sample, to have a statistically
significant result that is weak (small effect size).
b. Small effect size may indicate that the difference or
association is of little practical importance.
C. Confidence Intervals
1. An alternative to null hypothesis significance testing
(NHST).
2. May provide more practical information than NHST.
3. Confidence intervals allow us to determine the interval that
contains population mean difference 95% of the time.
D. Effect Size
1. The strength of the relationship between the independent
variable
and the dependent variable.
2. r family of effect size measures
a. Pearson correlation coefficient (r): values range from –1.0
to +1.0 (0 = no effect and +1/-1 =maximum effect).
b. Also includes other associational statistics such as rho, phi,
eta and the multiple correlation (R).
c. Can be reported as a squared or unsquared value.
i. Squared values (r2) indicate the percent of variance of
the DV that can be predicted from the IV, but give
small numbers that give an underestimated
impression of the strength or importance of the effect.

ii. Unsquared values (r) give a larger value and are
recommended for r family indices.
3. d family of effect size measures
a. Focuses on the magnitude of the difference rather than the
strength of the association.
b. Computed by subtracting the mean of the second group
from the mean of the first group and dividing by the pooled
standard deviation of both groups.
Manual by
c. All d family effect sizes express effect sizes in standard
deviation units.
d. Values usually vary from 0 to +/- 1.0, but can be > 1.0.
4. Issues about effect size measures.
a. d is not available on SPSS outputs but can be calculated
from information provided on SPSS outputs.
b. r and R are available on SPSS outputs.
c. Most journals now expect authors to discuss the effect size
as well as statistical significance.
E. Interpreting Effect Sizes
1. Table 6.5 provides guidelines for the interpretation of effect

sizes
based upon the effect sizes usually found in the behavioral
sciences and education.
2. The absolute meaning of large, medium, and small are
relative to
findings in these disciplines. Suggest using the following terms
instead:
a. Minimal in place of small.
b. Typical in place of medium.
c. Substantial in place of large.
3. Cohen’s (1998) examples of effect size:
a. Small = “difficult to detect”.
b. Medium = “visible to the naked eye”.
c. Large = “grossly perceptible”.
4. Effect size is not the same as practical significance.
a. Effect size indicates the strength of the relationship and is
more relevant to practical significance than statistical
significance.
b. However, effect size measures are not direct indexes of the
importance of a finding.
V. An Example of How to Select and Interpret Inferential
Statistics
A. Steps in the process:
1. Identify the research problem.
2. Identify the variables and their level of measurement.
3. State the research question(s).
4. Identify the type of each research question.
5. Select an appropriate statistic.

6. Interpret the results of the statistic.
a. Determine if the results were statistically significant.
b. If the results are statistically significant:
i. Determine the direction of the effect.
ii. Calculate and interpret the effect size.
iii. If necessary, calculate and interpret confidence
intervals to evaluate practical significance.
VI. Writing About Your Outputs
A. Methods Chapter
Manual by
1. Update methods to include descriptive statistics about the
demographics of the participants.
2. Add literature based evidence about the reliability and
validity of
measures/instruments.
3. Discuss if statistical assumptions were violated or not.
B. Results Chapter
1. Includes a description of the findings.
2. Include figures and tables to illustrate the findings.
3. Do not include a discussion of the findings in this section.
4. Results of statistics should include:

a. The value of the statistic (e.g. t = 2.05)
b. The degrees of freedom (and N for chi-square)
c. The p or Sig. Value (e.g. p = .048)
C. Discussion Chapter
1. Puts the findings in context to research literature, theory and
the
purposes of the study.
2. Explain why the results turned out the way they did.
Manual by Gene W.
Chapter 1 - Variables, Research Problems and Questions
Study Guide
OBJECTIVES:
1. Explain the difference between research problems, research
hypotheses, and research
questions.

2. Provide definitions for different types of variables.
3. Identify the research question, research hypothesis, and types
of variables used in a study.
4. Determine if a research question is a difference research
question, an associational
research question, or a descriptive research question.
5. Explain the relationship between the type of independent
variable used in a study and the
type of research question that can be answered (difference,
associational, descriptive).
6. Discuss how the type of research questions drives the
selection of the type of statistic.
7. Utilize the SPSS data editor and variable view features to
examine the variables of an
existing dataset.
TERMINOLOGY:
• research problem
• variable
o independent variable (active vs. attribute)
o dependent variable
o extraneous variable
• operational definition
• randomized experimental study
• quasi-experimental study
• non-experimental study
• factor
• grouping variable
• values (categories, levels, groups, samples)
• variable label

• value label
• research hypotheses
• research question
o difference research question
o associational research question
o descriptive research question
o complex research question (multivariate)
ASSIGNMENTS: See additional activities for assignment
examples.
Manual by
Chapter 1 – Variables, Research Problems and Questions
Chapter Outline
I. Research Problems: Statement about the relationships
between two or more
variables.
II. Variables
A. Definition: Characteristic of the participants or situation for
a study

1. Must be able to vary or have different values.
2. Concepts that do not vary are called constants.
3. Operational definition: defines a variable in terms of the
operations or techniques used to measure it or make it happen.
B. Independent Variables
1. Active (manipulated) independent variable: can be given to
participants within a specified period of time during the study.
a. Are not necessarily manipulated by the experimenter.
b. Treatment is always given after the study is planned.
c. Randomized experimental & quasi-experimental studies
must have active independent variables.
2. Attribute (measured) independent variable: preexisting
attributes
of the persons or their ongoing environment.
a. Cannot be manipulated by the experimenter.
b. Non-experimental studies have attribute independent
variables.
3. Other terms for independent variables:
a. factor
b. grouping variable
4. Inferences about cause and effect:
a. Designs with active independent variables (experimental,
quasi-experimental) can provide data to infer that the
independent variable caused the change or difference in the
dependent variable.
b. Designs with attribute independent variables (non-

experimental) should not be used to conclude a cause and
effect relationship between the independent variable and
the dependent variable.
5. Values of the independent variable:
a. Several options or values of a variable.
b. Also called: categories, levels, groups, samples
C. Dependent Variables
1. Presumed outcome or criterion that is supposed to measure or
assess the effect of the independent variable.
2. Must have at least two values, but usually have many values
that
vary from high to low.
Manual by
D. Extraneous Variables
1. Not of interest in a particular study but could influence the
dependent variable.
2. May also be called nuisance variables or covariates.
III. Research Hypothesis and Questions
A. Research hypothesis: predictive statements about the
relationship between

variables.
B. Research questions: similar to hypotheses, but do not make
specific
predictions.
1. Difference research questions: compare two or more different
groups on the dependent variable
a. Utilize difference inferential statistics (e.g. ANOVA or t-
test)
2. Associational research questions: find the strength of
association
between variables or to make predictions about a variable from
one or more variables.
a. Utilize associational inferential statistics (e.g. correlation,
multiple regression)
3. Descriptive research questions: summarize or describe data
without trying to generalize to a larger population of
individuals.
4. Complex research questions: involve more than two
variables at
a time.
a. Utilize complex inferential statistics.
b. May be called multivariate in some books.
IV. Sample Research Problem: The Modified High School and
Beyond (HSB)
Study
A. Research Problem: What factors influence mathematics
achievement?

1. Identify primary dependent variable
2. Identify independent and extraneous variables
3. Identify types of independent variables (active vs. attribute)
4. Identify the research approach (experimental, quasi-
experimental, non-experimental)
B. SPSS Variable View
1. Columns give information on database variables
a. Name shows the variable name
b. Label gives a longer description of the variable
c. Values shows assigned value labels
d. Missing identifies if certain values are designated by user
for missing values
C. SPSS Data Editor
1. Shows raw data
a. Variables are across the top (identified by short variable
names)
b. Participants are listed down the left side.
Manual by
D. Research Questions for the Modified HSB Study
1. Descriptive questions (Chapter 4)
2. To examine continuous variables for normality (Chapter 4).

3. Determine relationships between two categorical variables
with
crosstabulations (Chapter 8).
4. Associational questions (Chapter 9)
5. Complex associational questions (Chapter 9)
6. Basic difference questions (Chapter 10)
7. Complex difference questions (Chapter 11)
III. Research Hypothesis and QuestionsIV. Sample Research
Problem: The Modified High School and Beyond (HSB) Study
Manual by Gene W.
Chapter 2 – Data Coding, Entry, and Checking
Study Guide
OBJECTIVES:
1. Describe the steps necessary to plan, pilot test and collect
data.
2. Prepare data for entry into SPSS or a spreadsheet
3. Define and label variables.
4. Display your SPSS codebook (dictionary).
5. Enter data into SPSS or a spreadsheet.
6. Check accuracy of data entry using SPSS Descriptive
Statistics.

TERMINOLOGY:
• pilot study
• content validity
• coding
• dummy coding
• codebook
• define variables
• label variables
• missing values
• data entry form
Manual by
Chapter Outline
I. Plan the Study, Pilot Test, and Collect Data

A. Plan the study
1. Identify the research problem, question and hypothesis.
2. Plan the research design.
B. Select or develop the instrument(s)
1. Select from available instruments
2. Modify available instruments
3. Develop your own instruments
C. Pilot test and refine the instruments
1. Try out instrument on friends or colleagues
2. Conduct pilot study with a similar sample population
3. Utilize experts to check content validity of instrument items
D. Collect the data
1. Use methods appropriate for selected instruments
2. Check raw data before entering
3. Set “rules” for dealing with problematic responses.
II. Code Data for Data Entry
A. Rules for data coding (assigning numbers to values or levels
of a variable)
1. All data should be numeric.
2. Each variable for each case or participant must occupy the
same
column in the SPSS Data Editor.
3. All values (codes) for a variable must be mutually exclusive.
4. Each variable should be coded to obtain maximum
information.
5. For each participant, there must be a code or value for each
variable.
6. Apply any coding rules consistently for all participants.
7. Use high numbers (value or code) for the “agree”, “good”, or

“positive” end of a variable that is ordered.
B. Make a coding form: to streamline data entry processes
III. Problem 2.1: Check the Completed Questionnaires (follow
instructions in book)
IV. Problem 2.2: Define and Label the Variables (follow
V. Problem 2.3: Display Your Dictionary or Codebook (follow
VI. Problem 2.4: Enter Data (follow instructions in book)
VII. Problem 2.5: Run Descriptives and Check the Data (follow
I. Plan the Study, Pilot Test, and Collect DataII. Code Data for
Data EntryA. Rules for data coding (assigning numbers to
values or levels of a variable)B. Make a coding form: to
streamline data entry processesIII. Problem 2.1: Check the
Completed Questionnaires (follow instructions in book)IV.
Problem 2.2: Define and Label the Variables (follow
instructions in book)V. Problem 2.3: Display Your Dictionary
or Codebook (follow instructions in book)VI. Problem 2.4:
Enter Data (follow instructions in book)VII. Problem 2.5: Run
Descriptives and Check the Data (follow instructions in book)
Chapter2/Extra SPSS Problems.pdf

Manual by
Using the college student data.sav file, from
http://www.psypress.com/ibm-spss-intro-
stats/ (“Data Sets (ZIPS)” button) or the Moodle Web site for
this book, do the following
problems. Print your outputs and circle the key parts for
discussion.
1. Compute the N, minimum, maximum, and mean, for all the
variables in the college
student data file. How many students have complete data?
Identify any statistics on
the output that are not meaningful. Explain.
There are 47 students who have complete data. This value is
found by looking at
the value given for the Valid N (listwise).
The mean is not meaningful for nominal (unordered) variables.
In this example,
nominal variables include: gender of student, marital status, and
age group. The
mean for dichotomous variables coded as 0 and 1 can be
meaningful because the
means actually tell the percent of students that answered with a
“1” on their
survey. In this example, the following variables are

dichotomous: does subject
have children, television shows-sitcoms, television shows-
movies, television shows-
sports, television shows-news.
2. What is the mean height of the students? What about the
average height of the same
sex parent? What percentage of students are males? What
percentage have children?
Mean height of the students = 67.30 inches
Average height of same sex parent = 66.78 inches
Percentage of students that are male = 52.0%
Percentage of students with children = 52.0%
Manual by Gene W.
Chapter 3 – Measurement and Descriptive Statistics
Study Guide
OBJECTIVES:

1. Utilize frequency distributions to determine if data is
normally distributed.
2. Define the various levels of measurement (nominal, ordinal,
interval, ratio, etc.) and
recognize terms that are used interchangeably.
3. Distinguish between the types of measurement (e.g. nominal
vs. ordered, ordinal vs.
normal).
4. Utilize SPSS to generate descriptive statistics (frequency
distributions, measures of
central tendency, measures of variability) for a data set.
5. Select the appropriate descriptive statistics based upon the
level of measurement of the
data.
6. Describe the difference between parametric and non-
parametric statistics.
7. Describe the properties of the normal curve.
8. Determine whether data is normally distributed and describe
types of non-normality
exhibited (skewness, kurtosis, etc.).
9. Explain the relationship between the area under the normal
curve and probability
distributions.
10. Explain the purpose of converting data to a standard normal
curve and generating z-
scores.
TERMINOLOGY:

• frequency distribution
o approximately normally distributed
o not normally distributed
o negatively skewed
o positively skewed
• levels of measurement
o nominal (categorical, qualitative, discrete)
o dichotomous
o ordinal (ranks)
o interval
o ratio
o scale
o approximately normal (continuous, dimensional, quantitative)
o frequency tables
o bar charts
o histograms
o frequency polygons
Manual by Gene W.
o box and whiskers plot
• measures of central tendency
o mean
o median

o mode
• measures of variability
o range
o minimum
o maximum
o standard deviation
o skewness
o kurtosis
o interquartile range
• parametric vs. nonparametric statistics
• power
• normal curve
o area under the normal curve
o standard normal curve
o z scores
• kurtosis
Manual by
Chapter 3 – Measurement and Descriptive Statistics

Chapter Outline
I. Frequency Distributions
A. Definition: tally of the number of times each score on a
single variable
occurs.
B. Approximately normally distributed: there is a small number
of scores for
the low and high values and most of the scores occur in the
middle values
(distribution exhibits a “normal curve”).
C. Not normally distributed: distribution does not exhibit a
normal curve.
1. Negatively skewed: tail of the curve (extreme scores) is
elongated on the low end (left side).
2. Positively skewed: tail of the curve (extreme scores) is
elongated
on the high end (right side).
II. Levels of Measurement
A. Measurement: the assignment of numbers or symbols to
different
characteristics (values) of the variables.
B. Nominal Variables: numerals assigned to each category
stand for a name
of category.
1. Categories have no implied order or value.
2. Categories are distinct and non-overlapping.

3. Other terms for nominal variables:
a. Categorical
b. Qualitative
c. Discrete
C. Dichotomous Variables: have only two levels or categories.
1. May or may not have an implied order
2. Other terms for dichotomous variables:
a. dummy variables
b. discrete variables
c. categorical variables
D. Ordinal Variables: mutually exclusive categories that are
ordered from
low to high, but the intervals between categories may not be
equal.
1. Also includes ordered variables with only a few categories
(2-4)
2. Distribution of the scores is not normally distributed.
3. Other terms for ordinal variables:
a. Ranks
b. Categorical
E. Approximately Normal (or Scale) Variables: levels or scores
are ordered
from low to high and the frequencies of the scores are
approximately
normally distributed.
1. May be continuous (have an infinite number of possible
values
within a range).

2. If not continuous, should have at least five ordered values or
levels.
3. Other terms for approximately normal variables:
Manual by
a. interval – have ordered categories that are equally spaced
b. ratio – have ordered categories that are equally spaced and
have a true zero
c. continuous
d. dimensional
e. quantitative
F. How to Distinguish Between the Types of Measurement
1. Nominal versus ordinal variables:
a. Only two levels = treat as nominal in SPSS
b. Three or more categories and not ordered = nominal
c. Three or more categories and ordered = ordinal
2. Ordinal versus normal (scale) variables:
a. Five or more ordered levels with equal intervals and
approximately normal distribution = normal
b. Three or more ordered levels with unequal intervals and not
normally distributed = ordinal

III. Descriptive Statistics
A. Frequency Tables: tabulates the number of occurrences of
each level of a
variable as well as the number of missing values; also calculates
the valid
percent and cumulative percent for each level.
1. Nominal data: order of categories in table is arbitrary;
cumulative
percent column is not useful
2. Ordinal or approximately normal data: order of categories in
tables is shown from low to high; cumulative percent column is
useful.
B. Bar Charts: creates discrete (not connected) columns to
illustrate the
frequency distribution; appropriate for nominal data.
C. Histograms: similar to a bar chart, but there are no spaces
between the bars
which indicates a continuous variable underlying the scores.
D. Frequency Polygons: connects points between the categories;
best used
with approximately normal data (but can be used with ordinal
data).
E. Box and Whiskers Plot: useful for ordinal and normal data;
gives a
graphical representation of the distribution of scores.
1. Box: middle 50% of cases (those between the 25th and 75th
percentiles)

2. Whiskers: represent the expected range of scores.
3. Outliers: scores that fall outside the box and whiskers.
F. Measures of Central Tendency
1. Mean: the arithmetic average; statistic of choice for normally
distributed data.
2. Median: the middle score; appropriate measure for ordinal
data
or data that is skewed.
3. Mode: the most common category; can be used with any type
of
data, but is the least precise information about central tendency.
G. Measures of Variability: tells about the spread or dispersion
of scores.
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1. Range: highest score minus the lowest score; does not give an
indication of spread of scores for ordered data.
2. Standard Deviation: most common measure of variability;
based
upon the deviation of each score from the mean of all scores;
most appropriate for normally distributed data.
3. Interquartile Range: the distance between the 25th and 75th
percentiles (as shown in the box plot); appropriate for ordinal

data.
4. Nominal Data: variability measures are not appropriate;
rather
look at the number of categories and the frequency counts.
H. Conclusions About Measurement and the Use of Statistics
1. Normal data: utilize means and standard deviations for
parametric statistics.
2. Ordinal data: utilize median and nonparametric tests.
3. Nominal data: utilize mode or count.
IV. The Normal Curve
A. Properties of the Normal Curve: the normal curve is
theoretically formed
by counting an “infinite” number of occurrences of a variable.
1. Unimodal – the distribution has one hump which is in the
middle
of the distribution.
2. The mean, median and mode are equal.
3. The curve is symmetric (not skewed).
4. The range is infinite (the extremes never touch the X axis).
5. The curve is not too peaked or too flat and is neither too
short
nor too long (does not exhibit kurtosis).
B. Non-Normally Shaped Distributions
1. Skewness: one tail of the frequency distribution is longer
than
the other.
2. Mean and median are different.

C. Kurtosis
1. Refers to the shape of the curve.
2. Leptokurtic (positive kurtosis): frequency distribution is
more
peaked than normal.
3. Platykurtic (negative kurtosis): frequency distribution is
flatter
than normal.
D. Area Under the Normal Curve (Figure 3.10)
1. The normal curve is a probability distribution whose area is
equal to 1.0 and portions of the curve are fractions of 1.0.
2. Areas of the curves can be divided in terms of standard
deviations.
a. 34% of area under the normal curve is between the mean
and 1 standard deviation above or below the mean (thus,
68% of the area under the normal curve is within 1 standard
deviation to the left and right of the mean).
b. 13.5% of the area under the normal curve is accounted for
by adding a second standard deviation to the first (thus,
Manual by
95% of the area under the normal is within 2 standard

deviations to the left and right of the mean).
c. 5% of the area under the normal curve falls beyond 2
standard deviations to the left and right of the mean (thus,
this is why values not falling within 2 standard deviations
of the mean are seen as relatively rare events).
E. The Standard Normal Curve
1. A normal curve converted so the mean is equal to 0 and the
standard deviation is equal to 1.
2. This conversion allows comparison of normal curves with
different means and standard deviations.
3. z scores = units of the standard normal distribution
a. standard scores = term for raw scores that are converted to
the standard normal curve.
Manual by
Chapter 3 – Measurement and Description Statistics
Use the hsbdata.sav file from http://www.psypress.com/ibm-
spss-intro-stats/ (“Data Sets
(ZIPS)” button) to do these problems with one or more of these
variables: math

achievement, mother’s education, ethnicity, and gender. Use
Tables 3.2, 3.3, and the
instructions in the text to produce the appropriate plots or
descriptive statistics. Be sure
that the plots and/or descriptive statistics make sense (i.e. that
they are a “good choice” or
“OK”) for the variable.
3.1 Create bar charts. Discuss why you did or didn’t create
each.
• Select Analyze => Descriptive Statistics => Frequencies.
• Move math achievement, mother’s education, ethnicity, and
gender into the
Variables box.
• Select Charts => Bar Charts => Continue => OK.
Bar charts can be used with any of the four levels of
measurements, but it is better to use
frequency polygons or histograms if you have normally
distributed data. Each of these
types of plots displays the frequency or number of subjects on
the Y or vertical axis and
shows the levels or values of the variables on the X axis of the
plot. In histograms and
frequency polygons the bars or points are connected implying
that the levels of the
variable are ordered from low to high. In a bar chart the bars are
separated implying that
there might not be an order to the levels or categories of the
variable.
3.3 Create Frequency polygons. Discuss why you did or didn’t

create each. Compare
the plots in 3.1, 3.2, and 3.3.
• Select Graphs => Line. Click Simple and Summaries for
groups of cases
• Click Define.
• Move math achievement into the Category Axis box. => OK.
• Repeat the steps above, except this time instead of moving
math achievement, move
mother’s education in the Category Axis box. => OK.
Frequency polygons and histograms are similar. They are
designed for normally distributed data
but are okay to use with ordinal variables. A frequency polygon
connects the midpoints of the top
of each bar in a histogram. In other words, you can make a
frequency polygon from a histogram
by taking a straight edge and connecting the middle of each of
the bars.
3.5 Compute the mean, median, and mode. Discuss which
measures of central
tendency are meaningful for each of the four variables.
• Move the four variables into the Variables box.
• Statistics => Mean, Median, Mode => Continue => OK.
Although the mean, median, and mode are okay to use with
ordinal or normal data, the

mean is the most appropriate with normal data and the median is
best with ordinal data.
http://www.psypress.com/ibm-spss-intro-stats/
Manual by
Neither the mean nor the median are not meaningful with
nominal data. If you ask SPSS
to compute a mean or median for ethnicity, it will do so, but
because the ethnic categories
are not in any order, the result would not be interpretable. The
mode would tell you
which ethnic group was the largest. Similarly, the mode (and
median) tell you which
level of a dichotomous variable is most frequent. The mean of a
dichotomous variable
(e.g., gender) is the percent of participants who have the higher
value (i.e., female, in this
case).
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Fig. E.3

Fig. E.4
Manual by
Ch.3 Output 2.0
Frequencies
Statistics
math achievement
test mother's education ethnicity gender
N Valid 75 75 73 75
Missing 0 0 2 0
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Frequency Table
math achievement test
Frequency Percent Valid Percent
Cumulative
Percent
Valid -1.67 1 1.3 1.3 1.3
1.00 2 2.7 2.7 4.0
2.33 1 1.3 1.3 5.3
3.67 3 4.0 4.0 9.3
4.00 2 2.7 2.7 12.0
5.00 5 6.7 6.7 18.7
5.33 1 1.3 1.3 20.0
6.33 2 2.7 2.7 22.7
6.67 1 1.3 1.3 24.0
7.67 4 5.3 5.3 29.3
8.00 1 1.3 1.3 30.7
9.00 4 5.3 5.3 36.0

9.33 1 1.3 1.3 37.3
10.33 4 5.3 5.3 42.7
10.67 1 1.3 1.3 44.0
11.67 2 2.7 2.7 46.7
12.00 2 2.7 2.7 49.3
13.00 3 4.0 4.0 53.3
14.33 9 12.0 12.0 65.3
14.67 1 1.3 1.3 66.7
15.67 2 2.7 2.7 69.3
17.00 5 6.7 6.7 76.0
18.33 1 1.3 1.3 77.3
18.67 1 1.3 1.3 78.7
19.67 3 4.0 4.0 82.7
20.33 1 1.3 1.3 84.0
21.00 3 4.0 4.0 88.0
22.33 2 2.7 2.7 90.7
22.67 1 1.3 1.3 92.0
23.67 6 8.0 8.0 100.0

Total 75 100.0 100.0
Manual by
mother's education
Cumulative
Percent
Valid < h.s. 17 22.7 22.7 22.7
h.s. grad 31 41.3 41.3 64.0
< 2 yrs voc 2 2.7 2.7 66.7
2 yrs voc 5 6.7 6.7 73.3
< 2 yrs coll 7 9.3 9.3 82.7
> 2 yrs coll 5 6.7 6.7 89.3
coll grad 3 4.0 4.0 93.3
master's 3 4.0 4.0 97.3
MD/PhD 2 2.7 2.7 100.0

Total 75 100.0 100.0
ethnicity
Cumulative
Percent
Valid Euro-Amer 41 54.7 56.2 56.2
African-Amer 15 20.0 20.5 76.7
Latino-Amer 10 13.3 13.7 90.4
Asian-Amer 7 9.3 9.6 100.0
Total 73 97.3 100.0
Missing multiethnic 1 1.3
blank 1 1.3
Total 2 2.7
Total 75 100.0
gender
Cumulative

Percent
Valid male 34 45.3 45.3 45.3
female 41 54.7 54.7 100.0
Total 75 100.0 100.0
Manual by
Bar Charts
Manual by

Manual by
Fig. E.5
Fig. E.6
Manual by
Ch. 3 Output 3.3
GRAPH
/LINE(SIMPLE)= COUNT BY mathach.
Manual by

GRAPH
/LINE(SIMPLE)= COUNT BY maed.
Manual by
Fig. E.7
Ch. 3 Output 1.1
FREQUENCIES
VARIABLES=mathach maed ethnic gender
/STATISTICS= MEAN MEDIAN MODE
/ORDER= ANALYSIS
Frequencies
Statistics
math
achievement
test

mother's
education ethnicity gender
N Valid 75 75 73 75
Missing 0 0 2 0
Mean 12.5645 4.11 1.77 .55
Median 13.0000 3.00 1.00 1.00
Mode 14.33 3 1 1
Manual by
Frequency Table
math achievement test
Cumulative
Percent

Valid -1.67 1 1.3 1.3 1.3
1.00 2 2.7 2.7 4.0
2.33 1 1.3 1.3 5.3
3.67 3 4.0 4.0 9.3
4.00 2 2.7 2.7 12.0
5.00 5 6.7 6.7 18.7
5.33 1 1.3 1.3 20.0
6.33 2 2.7 2.7 22.7
6.67 1 1.3 1.3 24.0
7.67 4 5.3 5.3 29.3
8.00 1 1.3 1.3 30.7
9.00 4 5.3 5.3 36.0
9.33 1 1.3 1.3 37.3
10.33 4 5.3 5.3 42.7
10.67 1 1.3 1.3 44.0
11.67 2 2.7 2.7 46.7
12.00 2 2.7 2.7 49.3
13.00 3 4.0 4.0 53.3

14.33 9 12.0 12.0 65.3
14.67 1 1.3 1.3 66.7
15.67 2 2.7 2.7 69.3
17.00 5 6.7 6.7 76.0
18.33 1 1.3 1.3 77.3
18.67 1 1.3 1.3 78.7
19.67 3 4.0 4.0 82.7
20.33 1 1.3 1.3 84.0
21.00 3 4.0 4.0 88.0
22.33 2 2.7 2.7 90.7
22.67 1 1.3 1.3 92.0
23.67 6 8.0 8.0 100.0
Total 75 100.0 100.0
Manual by
mother's education

Cumulative
Percent
Valid < h.s. 17 22.7 22.7 22.7
h.s. grad 31 41.3 41.3 64.0
< 2 yrs voc 2 2.7 2.7 66.7
2 yrs voc 5 6.7 6.7 73.3
< 2 yrs coll 7 9.3 9.3 82.7
> 2 yrs coll 5 6.7 6.7 89.3
coll grad 3 4.0 4.0 93.3
master's 3 4.0 4.0 97.3
MD/PhD 2 2.7 2.7 100.0
Total 75 100.0 100.0
ethnicity
Cumulative
Percent

Valid Euro-Amer 41 54.7 56.2 56.2
African-Amer 15 20.0 20.5 76.7
Latino-Amer 10 13.3 13.7 90.4
Asian-Amer 7 9.3 9.6 100.0
Total 73 97.3 100.0
Missing multiethnic 1 1.3
blank 1 1.3
Total 2 2.7
Total 75 100.0
gender
Cumulative
Percent
Valid male 34 45.3 45.3 45.3
female 41 54.7 54.7 100.0
Total 75 100.0 100.0

Manual by Gene W.
Chapter 4 – Understanding Your Data and Checking
Assumptions
Study Guide
OBJECTIVES:
1. Describe the purpose of Exploratory Data Analysis (EDA).
2. Explain the purpose of statistical assumptions.
3. Select appropriate types of analyses and plots to conduct
EDA based upon the level of
measurement of the variable.
4. Utilize SPSS to conduct EDA.
5. Interpret SPSS output from EDA.
TERMINOLOGY:
• exploratory data analysis (EDA)
• statistical assumptions
o homogeneity of variances
o normality

• parametric tests
• robust
• non-parametric tests
• skewness
o positive skew
o negative skew
Manual by
Assumptions
Chapter Outline
I. Exploratory Data Analysis (EDA)
A. What is EDA?
1. The first step to complete after entering data and before
running
any inferential statistics.
2. Computing various descriptive statistics and graphs in order

to
examine your data.
a. Look for data errors, outliers, non-normal distributions, etc.
b. Determine if the data meets the assumptions of the statistics
you plan to use.
c. Gather basic demographic information about the subjects.
d. Examine relationships between the variables to determine
how to conduct the hypothesis testing.
B. How to do EDA
1. Generate plots of the data
2. Generate numbers from the data.
C. Check for Errors
1. Examine raw data before entering.
2. Compare some raw data against entered data.
3. Compare maximum and minimum values against the
allowable
ranges.
4. Examine the means and standard deviations to see if they
seem
reasonable.
5. Look to see if there is an unreasonable amount of missing
data.
6. Look for outliers.
D. Statistical Assumptions: explain when it is and isn’t
reasonable to perform
a specific statistical test.
1. Parametric tests

a. Usually have more assumptions than nonparametric tests.
b. Generally designed for use with data that exhibits
approximately normal distribution
c. S.some parametric tests are more robust in dealing with
violations of assumptions than others.
2. Nonparametric tests
a. Have fewer assumptions
b. Can often be used when assumptions for parametric tests
are violated.
E. Parametric Tests
a.
Manual by
Assumptions
Using the College Student data file, do the following problems.
Print your outputs and
circle the key parts of the output that you discuss.
4.1 For the variables with five or more ordered levels, compute
the skewness.

Describe the results. Which variables in the data set are
approximately normally
distributed/scale? Which ones are ordered but not normal?
• Select Analyze => Descriptive Statistics => Descriptives.
• Move student height, same sex parent’s height, amount of tv
watched per week,
hours of study per week, student’s current gpa, positive
evaluation-institution,
positive evaluation-major, positive evaluation-facilites, positive
evaluation-social
life, hours per week spent working in the Variables box.
• Options => Check Skewness (in addition to Mean, Std.
Deviation, Minimum,
and Maximum) => Continue => OK.
The Valid N (listwise) for the variables selected is 48. The
Means all seem reasonable
and within the expected range. The Minimum and Maximum
values are all with the
expected range, based on the codebook. The N for each
variable makes sense and only
two variables are missing values (positive evaluation-major and
hours per week spent
working).
The Skewness Statistic is utilized to determine which of these
variables are
approximately normally distributed. The guideline is that if the
Skewness Statistic is

between -1 and 1, the variable is at least approximately normal.
In this case, all the
variables with five or more ordered levels fall into that range
and would be considered
approximately normally distributed. For this dataset, the
ordinal variables with five or
more ordered levels (positive evaluation-institution, positive
evaluation-major, positive
evaluation-facilities, positive evaluation-social life) are all
approximately normally
distributed and we can assume they are more like scale
variables and we can use
inferential statistics that have the assumption of normality with
them. None of the
variables examined for this problem were not normal.
4.3 Which variables are nominal? Run frequencies for the
nominal variables and
other variables with fewer than five levels. Comment on the
results.
• Move gender of student, marital status, age group, does
subject have children,
television shows-sitcoms, television shows-movies, television
shows-sports,
television shows-news shows
The table titled Statistics provides the number of participants
for whom we have Valid
data and the number of Missing data. No other statistics were
requested because almost

all of them are not appropriate to use with nominal and
dichotomous data. Age group has
three ordered levels so it is ordinal and the median would be
appropriate.
Manual by
The other tables are labeled Frequency Table and there is one
for each of the variables
selected. The left-hand column shows the Valid categories (or
levels or values), Missing
values, and Total number of participants. The Frequency column
gives the number of
participants who had each value. The Percent column is the
percent who had each value,
including missing values. For example, in the marital status
table, 40.0% of ALL
participants were single, 36.0% were married, 22.0% were
divorced, and 2.0% were
missing. The Valid Percent shows the percent of those with
nonmissing data at each
value; e.g. 40.8% of the 49 students with valid data were single.
Finally, Cumulative
Percent is the percent of the subjects in a category plus the
categories listed above it.
Fig. E.8

Fig. E.9
Manual by
Ch. 4 Output 4.1
DESCRIPTIVES VARIABLES=height pheight hrstv hrsstudy
currgpa evalinst
evalprog evalphys evalsocl hrswork
/STATISTICS=MEAN STDDEV MIN MAX SKEWNESS.
Descriptives
Fig. E.10
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation Skewness
Statistic Statistic Statistic Statistic Statistic Statistic Std. Error
student height in inches 50 60.00 75.00 67.3000 3.93959 .163
.337

same sex parent's height 50 58.00 76.00 66.7800 5.10418 .333
.337
amount of tv watched per
week
50 4 25 11.98 6.096 .645 .337
hours of study per week 50 2 38 15.62 8.310 .950 .337
student's current gpa 50 2.4 4.0 3.172 .3907 .147 .337
positive evaluation,
institution
50 2 5 3.38 .945 .059 .337
positive evaluation, major 49 1 5 3.27 .953 -.115 .340
positive evaluation, facilities 50 1 5 2.76 1.061 -.136 .337
positive eval, social life 50 1 5 3.10 1.182 .031 .337
hours per week spent
working
49 0 50 26.12 14.857 -.516 .340
Valid N (listwise) 48

Manual by
Ch. 4 Output 4.3
FREQUENCIES VARIABLES=gender marital age children
tvsitcom tvmovies
tvsports tvnews
/ORDER=ANALYSIS.
Frequencies
Frequency Table
gender of student
Cumulative
Percent
Valid males 26 52.0 52.0 52.0
females 24 48.0 48.0 100.0
Total 50 100.0 100.0

marital status
Cumulative
Percent
Valid single 20 40.0 40.8 40.8
married 18 36.0 36.7 77.6
divorced 11 22.0 22.4 100.0
Total 49 98.0 100.0
Missing System 1 2.0
Total 50 100.0
Manual by
age group

Cumulative
Percent
Valid less than 22 17 34.0 34.0 34.0
22-29 18 36.0 36.0 70.0
30 or more 15 30.0 30.0 100.0
Total 50 100.0 100.0
does subject have children
Cumulative
Percent
Valid no 24 48.0 48.0 48.0
yes 26 52.0 52.0 100.0
Total 50 100.0 100.0
television shows-sitcoms

Cumulative
Percent
Valid no 18 36.0 36.0 36.0
yes 32 64.0 64.0 100.0
Total 50 100.0 100.0
television shows-movies
Cumulative
Percent
Valid no 32 64.0 64.0 64.0
yes 18 36.0 36.0 100.0
Total 50 100.0 100.0
Manual by

television shows-sports
Cumulative
Percent
Valid no 24 48.0 48.0 48.0
yes 26 52.0 52.0 100.0
Total 50 100.0 100.0
television shows-news shows
Cumulative
Percent
Valid no 27 54.0 54.0 54.0
yes 23 46.0 46.0 100.0
Total 50 100.0 100.0

Manual by Gene W.
Chapter 5 – Data File Management
Study Guide
OBJECTIVES:
1. Explain why data transformations might be necessary.
2. Count data.
3. Recode and relabel data.
4. Compute scale scores using either the numeric expression or
function features of the
SPSS Compute Variable command.
5. Check transformed data for errors and normality.
TERMINOLOGY:
• data transformation
• file management
• summated variable (composite variable, scale score)
• count
• recode
• reverse code
• relabel
• compute

Manual by
Chapter Outline
I. Problem 5.1: Count Math Courses Taken
A. Follow the directions in the book to use the Count command
to determine
how many math courses the participants took.
II. Problem 5.2: Recode and Relabel Mother’s and Father’s
Education
A. Recode is useful to either reduce the number of levels of a
variable or to
combine two or small groups or categories of a variable.
B. Follow the directions in the book to use the Recode command
to change
the levels of a variable.

III. Problem 5.3: Recode and Compute Pleasure Scale Score
A. A scale score can be computed by taking the average of
several variables.
B. Follow the directions in the book to compute a scale score
from several
items.
IV. Problem 5.4: Compute Parents Revised Education with the
Means Command
A. Follow the directions in the book to compute a new variable.
V. Problem 5.5: Check for Errors and Normality for the New
Variables
A. Follow the directions in the book to utilize the Descriptives
command to
check the new variables.
VI. Saving the Updated HSB Data File
A. Follow the directions in the book to save the recodes.
Manual by
Using the college student data, solve the following problems:

5.1. Compute a new variable labeled average overall evaluation
(aveEval) by
computing the average score (evalinst + evalprog + evalphys +
evalsocl)/4.
• Select Transform =>Compute type aveEval
• Type or click the formula shown above into the Numeric
Expressions box. =>
OK.
You should check the new variable to make sure you typed the
formula correctly. You
can visually compute a few by examining these four variables in
the Data View, and/or
running Descriptives on the new variable, aveEval, to check if
the results seem
reasonable. Valid N (listwise) = 49; Minimum = 1.75; Maximum
= 4.25; Mean =
3.1224.
5.3 Count the number of types of TV shows that each student
watches.
• Select Transform => Count
• Move tv sitcom, tvmovies, tvsports, and tvnews into the
Numeric Variables box.
• Name the Target Variable TVShows and label it Number of
types of TV shows
watched.

• Click Define Values => type “1” => Add => Continue => OK.
Each of the four types of TV shows are coded 1 for yes , watch
them, or 0 for nom don’t
watch, so the above commands count the number of different
types of shows watched,
from 0 (none of them) to 4 (all four). The COUNT can be
evaluated visually by
inspecting the Data View and/or by funning rfrequencies on the
new variable. The mean
number of types of TV shows watched is 1.98 and the mode is
2.00.14 students watch 1
type of TV show; 23 students watch 2 types of TV shows; 13
students watch 3 types of
TV shows.
Manual by
Fig. E. 11
Ch. 5 Output 5.1
COMPUTE aveEval= (evalinst+evalprog+evalphys+evalsocl)/4 .
EXECUTE .

Manual by
Fig. E.12
Fig. E. 13
Ch. 5 Output 5.3
COUNT
TVShows = tvsitcom tvmovies tvsports tvnews (1).
VARIABLE LABELS TVShows ‘Number of types of TV shows
watched ‘.
EXECUTE.
Appendix A.docAppendix A
Data Collection
This Appendix and Appendix B are designed to provide
introductory background information on developing a
questionnaire, collecting data with it, and getting the data ready
to enter and analyze. This chapter also explains the importance
of understanding your data, how to develop a questionnaire, and
how to set up data coding sheets.
There is a class assignment in this Appendix that will provide
an opportunity for your class to develop a short questionnaire,
collect data using it, and get the data ready for entry into SPSS.
If the class develops a set of questions, each person in the class
should answer them. There will be five standard questions and

then an opportunity for the class to add additional questions.
However, if you are using this text as an individual learning
tool or your professor wants to skip the topic of questionnaire
development, there is a set of data on the CD ROM (and
Appendix B) for you to enter into the SPSS data editor (and on
the disk).
Knowing Your Data
It is important for you to have a clear understanding of the data
in your data set. For example, in the class exercise one of the
variables will be height. You could look around the room and
intuitively know the data for that variable. You could line your
class up by height to see the distribution and you could make
fairly close approximations of statistics such as the average
(mean), middle person (median), and tallest minus shortest
(range). A few additional very tall people would skew the data
positively. Conversely, several extra short people would skew
the data negatively.
Students do not always have a clear understanding of the data
they analyze. In your research you may receive a data set from
a colleague, agency, or faculty member, where you do not really
know the underlying meaning of the variables. For example, in
chapter 6 you will receive a set of scores from the High School
and Beyond (HSB) data, which includes a variable labeled
visualization score. You will be told that it is a test of a
person's ability to determine how a three-dimensional object
would look if its spatial position were changed. But do you
really know what that means? Hopefully, when abstract
variables (such as visualization score, self-concept, or intrinsic
motivation) are used in a research report, how they were
measured (operationally defined) will be described completely
enough for you to grasp the meaning.
In this lab assignment (Appendix C), we will use data generated
by your class (or the similar data provided) so you will have a

sound understanding of the meaning of each variable. This kind
of understanding should help you learn SPSS and the statistical
tests that we will demonstrate better than data sets which may
not have as much meaning to you. You will also gain
experience entering data and labeling variables.
Developing a Questionnaire
This appendix uses a questionnaire as a data collection device
due to its relative simplicity. However, even a questionnaire
poses issues for a researcher. Appendix B presents several
topics related to questionnaire development and provides some
guidance about writing good items. Appendix B also has a
sample of the resulting questionnaire and a mock data set which
you can use if your class does not do the exercise suggested in
this appendix. If you will be developing a questionnaire in
class and are not already knowledgeable about doing that, we
suggest that you read Appendix B now.
Survey Questions Common to All Classes
In order to have a few variables to illustrate in the next chapter,
we suggest that your class use the following five common
variables: student’s height, same sex parent’s height, gender,
marital status, and age. The questions and suggested response
choices are as follows:
· What is your height in inches? ________
· What is the estimated height of your same sex parent?
________
· Gender (circle one number)
1.
Male
2.
Female

· What is your marital status? (circle one number)
1.
Single, never married
2.
Married
3.
Divorced, separated, or widowed?
· What age group are you in? (circle one number)
1. Less than 22
2. 22-29
3. 30 or moreClass Exercise
As a class, develop a short questionnaire that uses the five
questions above and asks other questions about your peers that
are of interest to you. With guidance from your instructor, add
approximately five questions to the five questions specified
above. Try to include a variety of questions. Some examples of
other questions the authors’ classes have used include:
How many hours a week do you watch TV? __________
What type of television shows do you watch? (Check all that
apply)
Sitcoms

Movies
Sports
News
I feel that the current program I am enrolled in is meeting my
needs. (circle one)
1. Strongly disagree
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
How many hours a week do you study? __________
Do you have children? ________Yes ________No
See Appendix B for a sample questionnaire.
Students often like to ask questions on surveys that require
respondents to answer with a phrase or a word. For example:
What is your favorite car? Or what state were you born in?
These questions are usually answered by spelling out your
response, such as Chevrolet or Ford. This type of response is
called alphanumeric. Although written answers can be very
useful in survey research, for this exercise, we recommend that
you do not use questions that require alphanumeric responses
because of the difficulty of coding and classifying these types
of responses.

Once your class develops a questionnaire of approximately 10
questions (the five above plus the five your class generates),
either a student or the instructor should make the questions
available to the class. The instructor can write the questions on
a flip chart, the board, or duplicate them. Each student should
have an answer sheet numbered 1 through 10, or have some
other method to record their responses.
After the class completes the questionnaire, there will be an
answer sheet for each student. The instructor may provide each
student with a set of raw data about the class. That is, each
student may receive data for every person in the class. If you
have a class of 20, you would begin this phase of the
assignment with 20 sets of answers for approximately 10
questions.
Next, you will prepare to enter the data on these answer sheets
into SPSS. This can be done in several ways.
Number each of your questionnaires. This will prevent
confusion if you drop or mix up the stack of questionnaires.
SPSS automatically labels cases from 1 through the last case or
subject. Therefore, it is a good idea for you to number the
questionnaires beginning with one, rather than using letters or
an alternative identification system. This process will also keep
the respondents' answers anonymous and thus protect against
violating the privacy of the human subjects in your research.
It is common for researchers to transfer the data from the
questionnaires to a coding sheet by hand before entering the
data into Excel, Word, or SPSS. This is helpful if there is not a
separately numbered answer sheet, if the responses are to be
entered in a different order than on the questionnaire, or
additional coding or recoding is required before data entry. In
these cases, you could make mistakes entering the data directly
from the questionnaires. On the other hand, you could make

copying mistakes or take more time transferring the data from
the questionnaires or answer sheets to the coding sheet. Thus,
there are advantages and disadvantages of using a coding sheet
as an intermediate step between the questionnaire and the SPSS
data editor. The data set for your class will be small and
straightforward enough that you will be able to keep track of the
data fairly easily going directly from your questionnaire into
the SPSS editor as explained in Appendix C.
Appendix C gives you a step by step approach of how to do this
for the first five variables. You then can use that knowledge to
add the additional five variables developed by your class. If you
did not collect data as a class project, use the data set provided
in Appendix C.
Interpretation Questions
1. In the first SPSS run of the class data, you noticed that the
average student height was 78 inches. What does this tell you
about your coding of the data?
2. Describe the advantages and disadvantages of developing and
using a coding sheet.
3. Why should you number your questionnaires?
PAGE
1
Appendix B.docAPPENDIX B
Developing a Questionnaire and a Data Set
Questionnaires are commonly used in research to collect data
that provide information about knowledge, perceptions, and
attitudes. The information collected reflects the subject’s
attitudes, knowledge, and so on at the time that the

questionnaire is completed, but may include recollections of the
past or predictions of future behavior. This appendix provides a
brief overview about developing a questionnaire. A more
complete discussion is provided by Salant and Dillman (1994)
and Dillman and Smyth (2007).
Deciding What Information You Want
Before writing any questions for a questionnaire, it is important
to think through how you will analyze the data. For example,
you might want to know what cars are the most popular among
your peers. An open-ended question to answer this would be:
“What is your favorite vehicle?” Your classmates may simply
write the name of their favorite vehicle. The following might
be responses to this question:
Volkswagen
Ford
Truck
Corvette
Yacht
Ski lift
Convertible
The use of the word "vehicle" allows responses like “ski lift”
and “yacht”. Thus, the way in which a researcher words a
question can greatly impact the type of answers respondents
provide.
Wording questions in a vague manner can cause other problems.
As a researcher you have to decide how to input the data into

SPSS. The vagueness of this “vehicle” example leaves the
researcher with many different responses, some of which are not
appropriate or the type of response that the question was
intended to generate. It can be helpful to have peers read your
questionnaire before proceeding. Many unclear questions or
problems with wording can be found and changed before you
end up with meaningless data.
Methods of Administration
After creating your questionnaire, the next step is to decide how
to give or administer it to the subjects. Questionnaires can be
administered using several different methods. The researcher
could collect information via the telephone, mail, e-mail,
individual face-to-
face, or group administration. The face-to-face method usually
is used when obtaining in-depth information requires more time
or explanation than is possible using mail or telephone. The
group method can be used in situations such as a club meeting
or a college classroom where a researcher or a surrogate
administers and collects the questionnaires from the group.
If your class is being taught via distance education the class
questionnaire data will most likely be collected via e-mail or
mail. If your class is being offered in the traditional classroom
format, then the data will most likely be collected via the group
method.
Sampling Techniques
There are a variety of sampling techniques that can be used.
There are two broad kinds of sampling: probability, and non-
probability. This appendix does not allow for a full explanation
of these methods. You can find more information about
sampling in a research methods book such as Gliner and Morgan
(2000).

A probability sample is defined as a selection of participants
where every person in a population has a known, nonzero
chance of being chosen. There are four probability sampling
techniques: simple random, systematic with a random start,
cluster, and stratified. These techniques are the most powerful
in that the data collected from samples using these methods are
more likely to be generalizable to larger populations.
A nonprobability sample is defined as a sampling technique
where there is not an equal chance for a participant to be
chosen. Thus, bias is usually introduced into the study. There
are several types of non-probability sampling techniques: quota,
convenience, and purposive are three. Unfortunately, most
student research, including the class questionnaire suggested in
chapter 4, use nonprobability sampling. With nonprobability
sampling, making generalizations beyond the group of study
may be problematic.
Writing Good Questions
In this section, we will describe briefly some of the main types
of questionnaire (or survey) items and questions.
Likert scales. One of the most common types of items used to
collect quantitative data is the Likert scale, which is sometimes
referred to as an “ordered response scale”. Most likely you
have used Likert scales many times. An example of a Likert
scale, which might have been used for the class questionnaire
is:
· Research Methods is my favorite subject. Circle the response
which most represents your feelings:
Strongly Agree
Agree
Undecided
Disagree

Strongly Disagree
When using a Likert scale the researcher has some choices. The
first choice is whether the Likert scale should include a middle
choice like undecided or should the question be written in a
manner that forces the respondent to either agree or disagree.
Another decision that needs to be made is whether to provide a
numerical scale under the wording. For example, the question
as stated above could be used with a numerical scale added.
Strongly Agree
Agree
Undecided
Disagree
Strongly Disagree
5
4
3
2
1
Researchers disagree about whether this type of numerical
addition is desirable. Sometimes researchers add the numerical
scale when they input the data but do not show this numbering
on the questionnaire. In standardized questionnaires it is
common to have a number of Likert type items and often the
words are at the top of the column and only the numbers are
opposite each item.
Semantic Differential scales. Another way to collect
quantitative data is with a semantic differential scale, which

uses bipolar adjectives (adjectives that are opposite of one
another). These adjectives can be Activity pairs, Evaluative
pairs or Potency pairs. An example of an Activity pair would
be “active – passive,” or “fast – slow.” Evaluative pairs include
words such as “good – bad,” or “dirty – clean.” Examples of
Potency pairs include comparisons such as “large – small” or
“hot – cold.” The evaluative type is used most often in
quantitative research.
An example of part of a semantic differential scale, which might
be used for your class questionnaire is:
· The research course I am currently taking is: (circle the
number that best reflects your view)
Bad
1
2
3
4
5
6
7
Good
Large
1
2
3
4
5

6
7
Small
Worthless
1
2
3
4
5
6
7
Valuable
When creating a semantic differential scale, it is best to switch
some of the positive choices from the right side to the left side.
Notice in the above example, the right side includes positive
words, such as “good” and “valuable.” The left side includes
words that are thought of as being more negative, such as “bad”
and “worthless.” The middle pair is switched, with the more
negative word, “small,” on the right and the more positive word,
“large,” on the left. Switching the words will help ensure that
the subject reads the words, and does not just circle “7” for each
answer.
Checklists. Another type of data collection for surveys is a
checklist. Checklists are words that are listed so the subject can
mark the ones that apply. Usually subjects are asked to mark all
that apply, thus there can be multiple words selected. An
example of a checklist is:
· What type of television shows do you watch? Please check all
that apply.
· Sitcoms

· Movies
· Sports
· News
Rankings. Rankings are another type of survey question. With
ranking questions subjects are asked to rank or place in order a
number of choices. Ranked items are relatively easy to make
and for participants to answer as long as they are asked to rank
only a few (for example three or four) items. However, ranking
items are not so easy to handle statistically. Two problems that
may occur are 1) the respondents may not rank all the items and
2) they produce ordinal data which eliminates the use of
parametric statistics (such as t test and correlation). An
example of a ranking scale is:
· Rank the following types of television shows in terms of how
much you would like to watch them (1 = most preferred, 4 =
least preferred). Please use each number only once and use all
four numbers.
Sitcoms
______
Movies
______
Sports
______
News

______
Open-ended. The last type of survey question discussed in this
appendix is the open-ended question. Unlike the other types of
survey questions discussed earlier, open-ended questions do not
provide choices for the subject to select. Each question is
worded so that the subject must generate an answer. An
example of an open-ended question would be:
· Do you have additional comments?
“How many hours a week do you watch television?” and “What
is your height in inches,” are also technically open–ended
questions, but they require only a single number for an answer.
There are also partially open-ended questions, which list several
possible response choices to pick from but also include an open-
ended choice such as:
· Other, please specify
___________________________________
Open-ended questions can be difficult to code. Also,
respondents may find open-ended questions to be more difficult
than other types of questions, because they require more
thinking, so they may skip them. There are also advantages to
using open-ended questions. On the positive side, open-ended
questions give subjects the opportunity to write whatever they
want, giving them more freedom to answer how they really feel
about a topic. Also, open-ended questions can give the
researcher more in-depth insight into how the subjects actually
feel in a more in-depth manner.
Sample Questionnaire, Codebook, and Data
The following figure and two tables include data to be used if
you did not develop a questionnaire and collect data in your
class as recommended in chapter 4. Figure B.1 is a sample of

how such a printed questionnaire might look. Table B.1 is the
codebook and Table B.2 is the raw data.
About You and Your Family
What is your height in inches?______
What is the estimated height of your same sex parent? ______
What is your gender? (circle one number)
1. Male
2. Female
What is your marital status? (circle one number)
1. Single, never married
2. Married
3. Divorced, separated, or widowed
What age group are you in? (circle one number)
1. Less than 22
2. 22-29
3. 30 or more
Do you have children?
1. Yes
2. No

How many hours a week do you watch television? ______
What type of television shows do you watch? Please check all
that apply.
· Sitcoms
· Movies
· Sports
· News
How many hours a week do you study? ______
What is your current grade point average? ______
Please rate the following four statements according to your
evaluation (circle one number).
I feel that the institution I am attending is great.
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
I feel that the current program in which I am enrolled is meeting
my needs.

2. Disagree
3. Neutral
4. Agree
5. Strongly agree
I feel the institution I am attending has good physical facilities.
2. Disagree
3. Neutral
4. Agree
5. Strongly agree
I feel that the institution I am attending provides mostly
horrible social activities.
2. Disagree
3. Neutral
4. Agree
5. Strongly agree

How many hours a week do you work? ______
Fig. B.1. Sample questionnaire.
Table B.1. Codebook
List of variables on the working file
Name Position
HEIGHT student height in inches 1
Measurement Level: Scale
Column Width: 8 Alignment: Right
Print Format: F8.2
Write Format: F8.2
PHEIGHT same sex parent's height 2
Print Format: F8.2
Write Format: F8.2
GENDER gender of student 3
Measurement Level: Nominal

Print Format: F8
Write Format: F8
Value Label
0 males
1 females
MARITAL marital status 4
Print Format: F8
Write Format: F8
Value Label
1 single
2 married
3 divorced
AGE age group 5
Measurement Level: Ordinal
Print Format: F8

Write Format: F8
Value Label
1 less than 22
2 22-29
3 30 or more
CHILDREN does subject have children 6
Print Format: F8
Write Format: F8
Value Label
0 no
1 yes
HRSTV amount of tv watched per week 7
Print Format: F8
Write Format: F8

TVSITCOM television shows-sitcoms 8
Print Format: F8
Write Format: F8
Value Label
0 no
1 yes
TVMOVIES television shows-movies 9
Print Format: F8
Write Format: F8
Value Label
0 no
1 yes
TVSPORTS television shows-sports 10

Print Format: F8
Write Format: F8
Value Label
0 no
1 yes
TVNEWS television shows-news shows 11
Print Format: F8
Write Format: F8
Value Label
0 no
1 yes
HRSSTUDY hours of study per week 12
Print Format: F8

Write Format: F8
CURRGPA student's current gpa 13
Print Format: F8.1
Write Format: F8.1
EVALINST evaluation of current institution 14
Print Format: F8
Write Format: F8
Value Label
1 strongly disagree
2 disagree
3 neutral
4 agree
5 strongly agree
EVALPROG evaluation of major program of study 15

Print Format: F8
Write Format: F8
Value Label
1 strongly disagree
2 disagree
3 neutral
4 agree
5 strongly agree
EVALPHYS evaluation of physical facilities of institution 16
Print Format: F8
Write Format: F8
Value Label
1 strongly disagree
2 disagree

3 neutral
4 agree
5 strongly agree
EVALSOCL negative evaluation of social life (This variable
has been
reversed, see Appendix G. It is now positive.)
17
Print Format: F8
Write Format: F8
Value Label
1 strongly disagree
2 disagree
3 neutral
4 agree
5 strongly agree
HRSWORK hours per week spent working 18

Print Format: F8
Write Format: F8
Table B.2. Appendix BData
height
pheight
gender
marital
age
children
hrstv
tvsitcom
1
67
66
1
.
1
0
18
1
2
72
72
0
2
3
1
4
0
3
61

62
1
3
3
1
25
1
4
71
71
0
1
2
0
6
0
5
65
63
1
1
1
0
8
1
6
67
68
0
1
2
0
10
0
7
69

74
0
1
2
0
24
1
8
75
73
0
2
3
1
14
0
9
62
65
1
3
3
0
5
1
10
61
64
1
2
3
1
5
1
11
64

63
1
2
2
1
10
1
12
64
64
0
3
2
1
6
0
13
70
65
0
1
2
0
20
0
14
63
62
1
2
3
0
7
1
15
64

60
1
1
2
0
4
1
16
63
60
1
2
3
1
10
1
17
65
58
1
2
3
1
10
1
18
71
76
0
2
3
1
6
1
19
72

75
0
2
3
1
9
0
20
68
65
1
1
2
0
8
1
21
75
73
0
1
1
0
20
0
22
67
66
1
3
1
1
4
1
23
69

74
0
1
1
0
10
1
24
67
68
0
1
1
0
8
0
25
64
63
1
1
1
0
14
1
26
64
62
1
3
2
1
8
1
27
70

65
0
3
2
1
17
0
28
64
64
0
1
1
0
8
0
29
70
75
0
1
1
0
14
0
30
72
75
0
3
1
1
12
0
31
64

62
1
2
2
1
15
1
32
71
73
0
3
3
1
10
0
33
67
66
1
2
3
1
8
1
34
63
60
1
2
3
1
6
0
35
69

68
0
1
1
0
20
1
36
68
66
0
3
2
1
15
1
37
64
63
1
1
1
0
15
1
38
70
68
0
1
1
0
7
1
39
71

74
0
2
3
1
6
0
40
72
73
0
1
1
0
20
1
41
60
62
1
3
2
1
10
1
42
65
63
1
2
1
1
15
1
43
72

72
0
1
1
0
24
1
44
63
62
1
1
2
0
18
1
45
75
74
0
2
2
1
10
1
46
71
68
0
1
1
0
24
1
47
65

59
1
2
2
1
5
0
48
69
67
0
2
2
1
15
1
49
63
62
1
2
2
1
10
0
50
67
66
1
3
3
0
22
1
tvmovies
tvsports

tvnews
hrsstudy
currgpa
evalinst
evalprog
evalphys
evalsocl
hrswork
1
0
0
5
3.2
3
3
4
2
0
0
1
0
24
2.6
3
.
3
2
43
1
0
0
20
3
4
4
4

4
.
0
1
1
17
3.1
3
4
3
3
37
1
0
1
18
3
3
3
4
4
35
0
1
0
10
3
3
2
3
4
38
0
1
1
12

3
4
4
4
5
0
0
1
0
10
3.6
3
3
3
5
20
1
0
1
8
3.4
3
4
3
4
40
1
0
1
17
3.5
2
2
2
3
38

1
0
1
38
3
4
3
3
2
28
0
1
0
35
2.4
2
3
3
4
40
0
1
0
20
3
4
4
4
5
0
1
0
1
10
3.8
3

4
3
2
30
1
0
0
12
3.3
3
4
5
3
45
1
0
0
35
3.7
3
3
3
2
43
0
0
1
30
3.4
3
3
3
4
34
0
1

0
22
3.7
4
4
4
4
36
0
1
0
20
3.6
4
3
4
5
30
1
0
0
12
3
3
3
3
4
29
0
1
1
2
2.5
3
4
2

4
0
1
0
0
30
3
2
2
2
3
21
0
1
1
12
3.4
2
2
2
4
23
0
1
1
10
2.5
4
3
3
3
26
1
0
0
15

3
2
2
2
3
12
1
0
0
30
3.1
2
2
3
3
15
0
1
1
18
2.4
4
3
2
3
5
0
1
1
12
2.9
2
1
3
4
35

0
1
0
4
3
3
3
2
3
7
0
1
1
8
2.9
2
2
5
2
10
1
0
0
15
3.2
3
3
3
3
40
0
1
1
20
3.8
2

3
5
3
42
0
0
0
12
3.8
1
1
5
1
40
0
0
1
22
4
1
2
3
3
35
0
0
0
7
2.8
2
2
1
2
0
0
1

1
12
2.9
3
3
2
2
28
1
0
0
18
3.2
2
2
4
2
0
0
1
0
13
3
2
3
3
4
40
0
0
1
10
3.6
1
2
3

1
50
0
1
0
8
2.8
4
4
1
4
40
1
1
0
20
3.2
2
2
5
1
20
0
0
0
10
3.4
2
3
3
1
15
0
1
1
8

2.9
1
1
3
2
38
1
0
0
15
3.2
2
2
5
1
10
0
1
0
10
3
2
2
3
2
20
0
1
1
5
2.9
4
5
2
3
37

0
0
1
12
3.6
1
2
5
1
40
0
1
1
18
3.3
2
2
3
2
35
0
0
1
10
3.1
3
2
5
3
10
1
1
0
20
3.9
1

1
4
1
20
133
1
Appendix C.doc
212
APPENDIX C
MAKING TABLES AND FIGURES
211APPENDIX C
Making Tables and Figures
Don Quick
Colorado State University
Tables and figures are used in most fields of study to provide a
visual presentation of important information to the reader. They
are used to organize the statistical results of a study, to list
important tabulated information, and to allow the reader a
visual method of comparing related items. Tables offer a way to
detail information that would be difficult to describe in the text.
A figure is a graphic or pictorial representation, such as a chart,
graph, photograph, or line drawing. These figures may include
pie charts, line charts, bar charts, organizational charts, flow
charts, diagrams, blueprints, or maps. Limit figures to situations
in which a visual helps the reader understand the methodology
or results. Use a table to provide specific numbers and summary
text, and use figures for visual presentations.
The meaning and major focus of the table or figure should be
evident to the readers without their having to make a thorough

study of it. A glance should be all it takes for the idea of what
the table or figure represents to be conveyed to the reader. By
reading only the text itself, the reader may have difficulty
understanding the data; by constructing tables and figures that
are well presented, readers will be able to understand the study
results more easily.
The purpose of this appendix is to provide guidelines that will
enhance the presentation of research findings and other
information by using tables and figures. It will highlight the
important aspects of constructing tables and figures using the
Publication Manual of the American Psychological Association,
Sixth Edition (2010)as the guide for formatting.
General Considerations Concerning Tables
Be selective as to how many tables are included in the total
document. Determine how much data the reader needs to
comprehend the material, and then decide if the information
would be better presented in the text or as a table. A table
containing only a few numbers is unnecessary, whereas a table
containing too much information may not be understandable.
Tables should be easy to read and interpret. If at all possible,
combine tables that repeat data, so that results are presented
only once.
Keep a consistency to all of your tables throughout your
document. All tables and figures in your document should use a
similar format, with the results organized in a comparable
fashion. Use the same name and scale in all tables, figures, and
the text that use the same variable.
In a final manuscript such as a thesis or dissertation, adjust the
column headings or spacing between columns so the width of
the table fits appropriately between the margins. Fit all of one
table on one page. Reduce the data, change the type size, or
decrease line spacing to make it fit. A short table may be on a

page with text as long as it follows the first mention of it. Each
long table is on a separate page immediately after it is
mentioned in the text. If the fit and appearance would be
improved, turn the table sideways (landscape orientation, with
the top of table toward the spine) on the page.
Each table and figure must be discussed in the text. An
informative table will supplement but will not duplicate the
text. In the text, discuss only the most important parts of the
table. Make sure the table can be understood by itself without
the accompanying text; however, it is never independent of the
text. There must be a reference in the text to the table.
Construction of the Table
Table C.1 is an example of an APA table for displaying simple
descriptive data collected in a study. It also appears in correct
relation to the text of the document; that is, it is inserted below
the place that the table is first mentioned either on the same
page, if it will fit, or the next page. (Fig. C.1 shows the same
table with the table parts identified.) The major parts of a table
are the number, the title, the headings, the body, and the notes.
Table C.1. An Example of a Table in APA Format for
Displaying Simple Descriptive Data
Table 1
Means and Standard Deviations on the Measure of Self-
Direction in Learning as a Function of Age in Adult Students
Self-directed learning inventory score
Age group
n

M
SD
20–34
35–40
50–64
65–79
80+
15
22
14
7
--a
65
88
79
56
--
3.5
6.3
5.6
7.1

--
Note. The maximum score is 100.
a No participants were found for the over 80 group.
Table Numbering
Arabic numerals are used to number tables in the order in which
they appear in the text. Do NOT write in the text “the table on
page 17” or “the table above or below.” The correct method
would be to refer to the table number like this: (see Table 1) or
“Table 1 shows…” Left-justify the table number (see Table
C.1). In an article, each table should be numbered sequentially
in the order of appearance. Do not use suffix letters or numbers
with the table numbers in articles. However, in a book, tables
may be numbered within chapters; for example, Table 7.1. If the
table appears in an appendix, identify it with the letter of the
appendix capitalized, followed by the table number; for
instance, Table C.3 is the third table in Appendix C.Table Titles
Include the variables, the groups on whom the data were
collected, the subgroups, and the nature of the statistic reported.
The table title and headings should concisely describe what is
contained in the table. Abbreviations that appear in the body of
the table can sometimes be explained in the title; however, it
may be more appropriate to use a general note (see also
comments on Table Headings). The title must be italicized.
Standard APA format for journal submission requires double
spacing throughout. However, tables in student papers may be
partially single spaced for better presentation.
Table 1
Means and Standard Deviations on the Measure of Self-
Direction in Learning as a
Function of Age in Adult Students

Inventory score
Age group
n
M
SD
20-34
35-40
50-64
65-79
80+
15
22
14
7
--a
65
88
79
56
--
3.5

6.3
5.6
7.1
--
Note. The maximum score is 100.
a No participants were found for the over 80 group.
Fig. C.1. The major parts of an APA table.
Table Headings
Headings are used to explain the organization of the table. You
may use abbreviations in the headings; however, include a note
as to their meaning if you use mnemonics, variable names, and
scale acronyms. Standard abbreviations and symbols for
nontechnical terms can be used without explanation (e.g., no.
for number or % for percent). Have precise title, column
headings, and row labels that are accurate and brief. Each
column must have a heading, including thestub column, or
leftmost column. Its heading is referred to as the stubhead. The
stub column usually lists the significant independent variables
or the levels of the variable, as in Table C.1.
The column heads cover one column, and the column spanners
cover two or more columns—each with its own column head
(see Table C.1 and Fig. C.1). Headings stacked in this manner
are called decked heads. This is a good way to eliminate
repetition in column headings but try to avoid using more than
two levels of decked heads. Column heads, column spanners,
and stubheads should all be singular, unless referring to a group

(e.g., children). Table spanners, which cover the entire table,
may be plural. Use sentence capitalization in all headings.
Notice that there are no vertical lines in an APA style table. The
horizontal lines can be added by using a “draw” feature or a
“borders” feature for tables in the computer word processor, or
they could be drawn in by hand if typed. If translating from an
SPSS table or box, the vertical lines must be removed.
The Body of the Table
The body contains the actual data being displayed. Round
numbers improve the readability and clarity more than precise
numbers with several decimal places. A good guideline is to
report two digits more than the raw data. A reader can compare
numbers down a column more easily than across a row. Column
and row averages can provide a visual focus that allows the
reader to inspect the data easily without cluttering the table. If a
cell cannot be filled because the information is not applicable,
then leave it blank. If it cannot be filled because the
information could not be obtained, or was not reported, then
insert a dash and explain the dash with a note to the table.
Notes to a Table
Notes are often used with tables. There are three different forms
of notes used with tables: (a) to eliminate repetition in the body
of the table, (b) to elaborate on the information contained in a
particular cell, or (c) to indicate statistical significance:
whole, including explanations of abbreviations used:
another source.
or cell of the table and is given a superscript lowercase letter,
beginning with the letter “a”:
an = 50. Specific notes are identified in the body with a
superscript.

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