Practical Applications of
Educational Research and
Basic Statistics
William Allan Kritsonis, PhD
Prairie View A&M University
Lisa Horton, PhD
Prairie View A&M University
1

Practical Applications of Educational
Research and Basic Statistics
William Allan Kritsonis, PhD
&
Lisa Horton, PhD
Published by National FORUM Journals
17603 Bending Post Drive
Houston, Texas 77095
Copyright 2007/2008 by William Allan Kritsonis, PhD
Except as permitted under the United States Copyright Act Of 1976, no part of this
professional publication may be reproduced or distributed in any form or by any
means, or stored in a database or retrieval system, without the proper written
permission of Dr. William Kritsonis. Absolutely no unauthorized reproduction of this
text.
ISBN: 0-9770013-4-2
Library of Congress Cataloging in Publication Data
$79.00 (United States)
$89.00 (Canada)
$99.00 (All others)
Published in the United States of America
2

Practical Applications of Educational
Research and Basic Statistics
Author
William Allan Kritsonis
PhD Program in Educational Leadership
Prairie View A&M University
Member of the Texas A&M University System
Prairie View, Texas
Lisa Horton
PhD Program in Educational Leadership
Prairie View A&M University
Member of the Texas A&M University System
Prairie View, Texas
3

Dedication
To all our students, past, present, and future. We wish to thank all the
people who devotedly concerned themselves with our professional and
personal development and improvement
ACKNOWLEDGEMENTS
The purpose of the text is to provide content and knowledge in the area of
research with students at both the master’s and doctoral levels.
A list of acknowledgements and credits is provided in the Partial Listing
of Selected References and Acknowledgements at the end of this text.
CONTENTS
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Page
PART I: Practical Applications of Educational Research and Basic Statistics ....6
Chapter 1: Development of Research .................................................................7
Chapter 2: Historical Research .........................................................................14
Chapter 3: Descriptive Research ......................................................................18
Chapter 4: Experimental and Quasi-Experimental Research ............................22
Chapter 5: Qualitative Research .......................................................................30
Chapter 6: Methods and Tools of Research ......................................................33
Chapter 7: Descriptive Statistics and Normal Distribution ...............................39
Chapter 8: Inferential Data Analysis ................................................................55
Chapter 9: Parts of the Research Proposal .......................................................61
Chapter 10: Parts of a Field Study ....................................................................67
Chapter 11: General Statistics Information ......................................................73
Chapter 12: Types of Statistical Data ...............................................................77
Page
Chapter 13: Descriptive Statistics ....................................................................81
Chapter 14: Types of Distributions ..................................................................88
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Chapter 15: Formulas .......................................................................................90
Chapter 16: Understanding and Using Statistics. The Basics ..........................92
Chapter 17: Getting Started With Research: Avoiding the Pitfalls ...................96
Chapter 18: Ethics and Research ......................................................................99
Chapter 19: Ethics in Research on Human Subjects and the role of the
Institutional Review Board. Frequently Asked Questions .............................101
Chapter 20: Working with the IRB Suggested Frame
of Mind for Researchers .................................................................................104
Chapter 21: Research, Writing & Publication ...............................................106
PART II: Fundamental Terms in Educational Research
and Basic Statistics .......................................................................................110
Fundamental Terms in Educational Research and Basic Statistics .................111
PART III: Partial Listing of Selected References
and Acknowledgements ...............................................................................144
Partial Listing of Selected References and Acknowledgements .....................145
PART IV: About the Authors .....................................................................154
6

PART I:
Practical Applications of
Educational Research
and Basic Statistics
7

Chapter 1
Development of Research
1. Key Points
a. Observations
b. Experience
c. Intuition
d. Hand me down
e. Revelation
f. Definition or Decree
g. Philosophy or Logic
h. Instinct
2. Centuries ago, medicine men, religious authorities, and elders were
knowledge sources? (No one questioned them.)
3. With time, people began to observe orderliness and cause and effect
relationships in the universe. Events were recorded and analyzed.
4. Some things could be predicted. Events could be predicted in relation to
the time of year and the seasons.
5. This brought on a conflict.
a. Religious authority versus curious thinkers
b. Authority versus empirical evidence
c. Elders versus personal experience
6. People eventually began to think systematically. A few great thinkers led
the way.
8

7. Aristotle (Ancient Greece)
a. First approach to reasoning.
b. Deductive Method - moving from general assumptions to specific
Syllogism
1) Major Premise: All men are mortal.
2) Minor Premise: Socrates is a man.
3) Conclusion: Socrates is a mortal.
8. Centuries later-Francis Bacon
a. Direct observation of phenomena
b. Arriving at conclusions or generalizations through evidence of many
individual observations led to inductive reasoning.
9. Combining the deductive and inductive methods of reasoning results in
the emerging of the scientific method or scientific approach.
10. In 1930, John Dewey detailed the scientific method or scientific
approach as follows:
a. Identify and define a problem
b. Formulate a hypothesis
c. Collect, organize, and analyze data
d. Formulate conclusions
e. Verify or reject hypothesis, modify hypothesis
There are many ways to specifically approach the scientific method and
there are numerous generalizations of scientific approaches.
The deductive approach is hypothesizing and anticipating the
consequences of events.
11. Researchers go back and forth--inductive-deductive-inductive-deductive.
An example would be to hypothesize-observe and collect data-reject
hypothesis-reformulate new hypothesis-observe and collect more data-
partially accept hypothesis-then collect more data.
9

12. Science
1) Definition: An approach to the gathering of knowledge, rather than a
field of study.
2) Two Functions of Science
i. Develop theory
ii. Test hypotheses deduced from theory
13. The Way a Scientist Works
a. Empirical Approach - collect data
b. Rational Approach - logical deductive reasoning
14. Researcher attempts to develop theories and predict events in
hopes of possibly controlling events.
a. Piaget’s Theories - Cognitive development
b. Behavior of gases - Air-conditioning, refrigeration
c. Atomic Theory - Nuclear power
d. Celestial Theory - Space travel, NASA, Satellites, and other
technical advances.
15. Two Types of Hypotheses
a. Research Hypothesis (Alternative Hypothesis) (Symbol=Ha)
1) Affirmative statement that predicts a single outcome
2) Examples:
i. Teaching Method A is better than Teaching Method B.
ii. Cigarette smoking causes heart disease.
iii. Extra curricular activities improve academic performance.
iv. Computer Assisted Instruction improves academic
achievement.
v. Homework improves academic achievement.
b. Null Hypothesis (Symbol=Ho)
1) This hypothesis is stated negatively so that the logic of statistical
analysis can be applied.
10

2) The null hypothesis is saying the difference, if any, is due to
chance.
3) Rejecting the null hypothesis with a probability statement would
support the research hypothesis (Ha).
4) Examples:
i. There is no difference in heart disease between smokers and
nonsmokers.
ii. There is no difference in academic achievement between
Method A and Method B.
iii. There is no difference in grades between CAI students and
non-CAI students.
iv. There is no difference in academic achievement due to
participation in extra curricular activities.
16. Sampling Definitions
a. Population-----------------------parameter
b. Sample---------------------------statistic
c. Sample: a small proportion of a population selected for observation
and analysis
d. Statistic: a value from a sample used to infer the parameters of a
population
17. Types of Samples
a. Simple Random Sample: every subject has an equal chance to be
selected
b. Systematic Sample: every nth number
c. Stratified Random Sample: subdivide population and select sample
proportionally-A random sample of each of the subgroups is done.
d. Cluster Sample: most complex of all samples, used for very large
groups; costly and take time.
50 states---------------------Randomly choose 20 states.
20 states---------------------Randomly choose 80 counties.
80 counties------------------Randomly choose 50 school districts.
50 districts------------------Randomly choose 10 teachers from
each of the 50 school districts.
Total Sample 500 teachers
11

e. Non-probability Sample: (Use subjects available)
f. Purposive Sample: participants are chosen not by chance but
intentionally to yield data for evaluation purposes
18. Sample Size (Test for Beta, or use a table.)
a. The larger the sample, the less error.
b. The larger the sample, the better the sample represents the
population.
c. In utilizing a survey, be certain to have a large sample.
d. 32 (in a sample) is the magic number statistically, but
e. Try to obtain more (with randomness)
19. Purposes of Educational Research
a. Fundamental or Basic: The purpose of this laboratory-type of
research is solely to gain new knowledge. This research is often
referred to as the search for knowledge for knowledge’s sake.
b. Applied: The purpose is to improve a product (software, textbook,
etc.) or process (teaching, learning, etc.)- testing a theoretical concept
in a real actual problem situation. Most educational research is applied
research. With the passing of time, basic research usually spurns
further applied research. New knowledge gained eventually becomes
useful and lends to advances in knowledge, which then directs more
applied research to take place.
c. Action: The purpose and focus are on immediate application-not on
development of theory. The focus is on the here and now in a local
setting.
20. Two ways to Classify Research
a. Quantitative Research: (Measuring)
1) Data are analyzed in terms of numbers.
2) Educational, medical, and agricultural professions use this type of
classification.
b. Qualitative Research: (Judging)
1) People and events are described without numerical data. This
research consists of a rich, literal description in a prose form.
12

2) Interviews of people, students, and other sources are used to collect
information. Research is written in prose form.
21. Assessment: Fact-finding activity that describes existing conditions
22. Evaluation: Fact-finding with judgment added
23. Types of Educational Research
a. Historical
1) A description of what was.
2) Application of the scientific method to the use of historical data to
answer historical questions or to test historical hypotheses.
b. Descriptive
1) A description of what is.
2) Application of the scientific method to the acquisition and use of
current data to describe current conditions
c. Experimental: description of what will be where certain variables
are carefully manipulated.
d. Qualitative: uses non-quantitative methods to describe what is
1) Basically, data are interpreted without numerical analysis.
2) Interviews, videos, and other methods are used to gather
information.
13

SUGGESTED ACTIVITIES:
1. Divide into groups of 3-4. Discuss the following question: What is your
definition of research, the steps you feel are needed to be taken to do
research, and what types of research have you read or become familiar
with in your profession and your educational experience? Share your
group activity with the entire class.
2. Each group should answer the following: What two things would you
like to see changed in your profession or questions answered? How
could you use research to address that change? What types of research
could you use to answer your questions? How would you set up the type
of research needed to answer these questions? Share your group activity
with the entire class.
3. Develop a research and a null hypothesis for each of the research ideas
identified in the previous activity. Share your group activity with the
entire class.
WEBSITES:
San Jose State University – http://www2.sjsu.edu/depts/itl/graphics/induc/ind-
ded.html
14

Chapter 2
Historical Research
Key Points
1. Is an attempt to arrive at conclusions concerning causes, effects or trends
of past occurrences that may help explain past and present events and
predict future events.
2. Historical research describes what was.
3. Historical research involves investigating, recording, analyzing and
interpreting events of the past.
4. Sources of Information
a. Primary Sources
1) Records and reports of legislative bodies, records and/or memoirs
of superintendents, school newspapers, curriculum guides, grade
books, along with other sources.
2) Interviews with superintendents, school board members,
principals, teachers, and students.
3) Relics, such as buildings, furniture, textbooks and examinations.
b. Secondary Sources
1) Reports of a person who relates the testimony of an eyewitness.
2) Encyclopedia, textbooks and newspaper accounts
5. Characteristics of Historical Research
a. Guided by hypotheses or questions to be answered
b. Systematic collection of data
c. Objective evaluation of data
15

d. Limited to available data
e. Explanation—not just rehashing of the past—explains why it
happened as it did
f. May investigate individuals, ideas, movements, institutions, cultural
circumstances, and movements
g. Employs the scientific method
6. Limitations/Problems with Historical Research
a. Generalizations may not be feasible.
1) Too many uncontrollable factors.
2) Key individuals wield too much influence.
3) Situations won’t repeat themselves.
b. Historical documents may not be reliable.
1) Were not written as objects of research
2) No objectivity
3) Often second—not firsthand information
4) Information is often incomplete.
c. History is not verifiable by observation or experimentation.
d. Significant variables cannot be manipulated.
e. Lack of direct observation and control of variables
f. Uniqueness cannot be replicated.
7. Steps in Historical Research
a. Define the problem
b. Formulate the hypothesis or questions to be answered
c. Collect data
1) Primary sources
2) Secondary sources
d. Analyze the data
1) External criticism—authenticity
i. Was this person really present?
ii. Is this a real document from that time period?
2) Internal criticism—accuracy
i. Did the person give an unbiased account of what happened?
16

ii. Is the document telling a true story or did the author
have a “hidden agenda”?
iii. Did anyone tamper with the document
e. Synthesize data
1) Conclusions
2) Generalizations
3) Explanation or hypothesis
f. Report findings and conclusions
SUGGESTED STUDENT ACTIVITIES:
1. In groups of 3-4 locate the answers to the following questions:
MAJOR QUESTION: How does your university compare today with the
institution which was 50 years ago?
SUBQUESTIONS:
A. What academic programs were offered sixty years ago that were related
to education?
B. What types of school facilities were available then?
C. What was the type of curriculum offered to students?
D. How large was the student body?
E. What was the ethnic make-up of the student body?
F. What role did the school play in the community, state and nation?
G. How many professors/instructors were employed?
Compare and contrast the data from 50 years ago with today.
17

Chapter 3
Descriptive Research
KeyPoints
1. Characteristics of Descriptive Research
a. Nonexperimental: deals with natural, not contrived relationships
b. Variables are not manipulated.
c. Ex post facto—a thing done afterward
d. Involves disciplined inquiry (scientific method)
e. Uses logical methods of inductive-deductive reasoning to arrive at
generalizations
f. Employs valid statistical procedures in collecting and tabulating data
g. Employs valid statistical procedures in reporting results
h. Adds to the body of knowledge
2. Three Types of Descriptive Research
a. Descriptive Research
1) This type of research is purely descriptive.
2) There is no hypothesis.
3) Researcher is just collecting data.
4) Example: 65% of principals are male; 35% are female. The
average age of principals is 43; the average age of teachers is 38.
18

b. Correlational Research
1) In this research, the researcher is measuring the relationship
between two or more variables.
2) The relationship between the variables may be strong, weak, or
there could be no relationship.
3) Correlational studies can be used to predict.
Example: ITBS scores and CAT scores have a correlation
Coefficient of .8.
c. Causal-Comparative Research
1) This type of research is interested in suggesting causation for the
findings. It is aimed at discovering potential causes for a pattern by
comparing a treatment group against a non-treatment group.
2) One should not say that a variable was the cause of an action,
unless all other variables were controlled. Just identify the
limitations of the study.
3) There is no experimental manipulative.
4) Example: Collective bargaining apparently had some effect on
teacher job satisfaction since satisfaction levels were higher after
collective bargaining than they were prior to collective bargaining.
19

SUGGESTED STUDENT ACTIVITIES:
1. Divide into groups of four to five students. Develop a chart listing the
different types of descriptive research. Compare and contrast each type
of research. Provide at least three examples of each type.
TYPE OF SIMILARITIES DIFFERENCES WITH EXAMPLES
DESCRIPTIVE WITH OTHER OTHER TYPES OF
RESEARCH TYPES OF DESCRIPTIVE
DESCRIPTIVE RESEARCH
RESEARCH
1. Surveys Very similar to polls Use of a large number a. restaurant
in that you collect of cases to describe to a questionnaire
data according to a general population. b. general
set of questions. You can collect data on satisfaction
attitudes as well as survey for
other practices, products
occurrences, etc. Polls purchased
are usually much c. (add more to
smaller and are the the list)
collection of attitudes.
2.
3.
4.
5.
6.
7.
8.
2. Describe how you can use both activity analysis and trend analysis to
determine the types of teachers that will be needed in the next five years
for both an urban and rural school district. Look at factors of the individ-
ual’s job as well as the growth trends/declines and population changes
(increase in retirees opposed to school age children) for the area. Select
either an elementary, middle school or high school you are familiar with
and use both types of descriptive research methods to determine what
types of staff patterns would be needed for your school.
20

Chapter 4
Experimental and Quasi-Experimental Research
Key Points
1. Definition: determining what will happen under certain circumstances—
a method of hypothesis testing—If this is done, what will happen?
a. Immediate purpose: “prediction” in a local setting
b. Ultimate purpose: “generalization” to a larger population
2. Law of the Single Variable: If all variables are held constant except
one, any changes in the outcome are due to changes in that one variable.
3. Experimental Grouping
a. Experimental Group vs. Control Group
1) Experimental Group: group exposed to variable under
consideration
2) Treatment Group: same as experimental group
3) Control Group: group not exposed to variable under consideration
b. Different Levels of the Same Variable: Subjects may also be
grouped according to type of treatment, not just absent of
treatment.
4. Variables
a. Definition: conditions or characteristics of the experiment that the
experimenter manipulates, controls, or observes
b. Independent Variable: variable manipulated by the researcher for
grouping.
1) Treatment Variable: factor that can be controlled by the researcher
2) Organismic Variable: attribute of the subjects that cannot be
controlled.
21

c. Dependent Variable: outcome; condition or characteristic that
appears, disappears, or changes according to manipulation of the
independent variable (Results).
d. Confounding Variable: aspect of a study that can influence the
dependent variable, which can be confused with the effects of the
independent variable.
1) Intervening Variable: aspect of a study that may modify the effect
of the independent variable upon the dependent variable.
2) Extraneous Variable: uncontrolled aspect of a study that is similar
in effect to the independent variable and may render subjects’
grouping invalid.
5. Experimental Validity
a. Internal Validity: extent to which the independent variable, not
extraneous variables, has a genuine effect on the dependent
variable.
b. External Validity: extent to which variable relationships established
by the study can be generalized to other settings.
6. Threats to Internal Validity
a. Maturation: change in subject(s) over time
b. History: events in the course of the study that may influence the
dependent variable
c. Testing: learning to take tests by taking tests
d. Unstable Instrumentation: use of unreliable data gathering devices
e. Statistical regression: regression to the mean: extremely low or high
scores tend not to repeat themselves.
f. Selection bias: nonequivalence of groups due to poor selection
22

g. Interaction of Selection and Maturation: When subjects can choose
the group to which they will belong, the variable that directed their
choices may have undue influence on the dependent variable.
h. Experimental Morality: loss of subject(s).
i. Experimenter Bias: If the researcher must evaluate a subject, prior
knowledge of the subject may have undue influence on the
researcher’s judgment.
7. Threats to External Validity
a. Interference of Prior Treatment: carryover of subjects’ knowledge
or skill from a previous situation that may be mistaken for an effect
of the independent variable.
b. Artificiality of the Experimental Setting: condition in which the
experimental setting is so controlled that it does not adequately
imitate the real-life situation for generalizations to be made.
c. Interaction Effect of Testing: condition in which a pre-test may
sensitize subjects to concealed purposes of the study and serve as a
stimulus to change.
d. Sampling Deficiencies: error or inability in random selection.
e. Lack of Treatment Verification: condition in which the treatment
was not applied in the manner prescribed by the study.
f. John Henry Effect: subjects work harder because they realize they
are competing with others.
g. Hawthorne Effect: subjects work harder because they are getting
attention. This is due to researchers giving them extra attention.
The experimental model comes from agricultural research.
8. Controlling Threats to Experimental Validity
a. Remove the Variable: variable is not considered in results.
23

b. Matching cases: selecting pairs with identical characteristics and
assigning them to different groups
c. Balancing Cases: assigning subjects to each group so that overall
group means and variances will be equal
d. Analysis of Covariance: statistical method that permits the
experimenter to eliminate initial differences in the experimental
groups
e. Random Selection: assignment to experimental groups by pure
chance; best way to make study valid
f. It is difficult to eliminate all extraneous variables, therefore it is best
to neutralize them. Remember, neutralize not eliminate!
9. Experimental Design
a. Definition: procedures of the study that enable valid conclusions by
controlling the following:
1) Selection and assignment of subjects
2) Control of variables: independent and confounding
3) The gathering and treatment of data
4) Development of hypothesis
5) Statistical testing of hypotheses
b. Purpose: elimination or neutralizing of threats to experimental
validity
10. Three Types of Experimental Designs
a. Pre-Experimental Design: provides no way for equating groups that
are used
b. True-Experimental Design: uses random selection for equating
groups that are used
c. Quasi-Experimental Design: used when random selection is not
available
24

11. In studying experimental design, the following Campbell and
Stanley symbols are used:
a. R random assignment of subjects
b X exposure of a group to a treatment
.
c. C exposure of a group to a control or placebo condition
d O observation or test administered (data gathered)
.
12. What makes a good study?
a. Having a control group and
b. Using random selection
13. Pre-experimental Designs
a. The One-Shot Case Study Design
1) X O
2) No random selection and no control group
b. The One-Group, Pretest, Posttest Design
1) O X O
2) No random selection, no control group, and interference of
variables
c. The Static-Group Comparison Design
1) X O
C O
2) No random selection
25

Pre-experimental design, the least adequate of designs, is
characterized by the lack of a control group or a lack to provide
for the equivalence of one.
14.True Experimental Design
a. The Posttest-Only, Equivalent-Groups Design
1) R X O
R C O
2) Has random selection; has control group
b. The Pretest-Posttest, Equivalent-Groups Design
1) R O X O gain (X) = O – O (pretests)
R O C O gain (C) = O – O (posttests)
2) Has random selection; has control group
c. The Solomon Four-Group Design
1) R O X O
R O C O
R X O
R C O
2) Has random selection; has control group
3) Difficult to find enough subjects
15.Quasi-Experimental Designs
a. The Pretest-Posttest Nonequivalent-Groups Design
1) O X O
O C O
2) No random selection
26

3) Pretest is used as covariate.
b. The Time-Series Design
1) O O O O X O O O O
2) No random selection
c. The Equivalent Time-Samples Design
1) O X O X O X O X O
2) No random selection
d. The Equivalent Materials, pretest, Posttest Design
1) O X O O X O
2) No random selection
3) Can be conducted with just one group or two separate groups
16. Factorial Designs: used when more than one independent variable is
involved
SUGGESTED STUDENT ACTIVITY:
1. Develop a Study (What problem do you want to address or solve?)
2. Why would I do it?
3. What do I already know or what has already been done on this problem?
4. What are your hypotheses/Research Question? (Research and null)
5. What would you do to conduct the research? (Steps, who to talk to,
permission for research, what instruments to collect data?)
6. Who are your participants?
7. How will you collect the data?
8. How will you interpret the data?
27

28

Chapter 5
Qualitative Research
Key Points
1. Qualitative research is sometimes called naturalistic inquiry.
2. The main reason that we have qualitative research is to explain
phenomena.
3. Qualitative research is done often as supplemental research.
4. Three Data Collection Methods of Qualitative Research
a. Interview: Teachers, secretary, janitors, and other individuals in the
school.
b. Observations: Observe what goes on in gyms, cafeteria, library,
classrooms, and hallways.
c. Analyze written documents and records: test scores, attendance
records, discipline reports—suspension and expulsion ratio—
When you analyze these, you often employ quantitative steps, such
as more than half, 60% etc.
5. Triangulation is the use of multiple data collection techniques. For
example, it could include interviews, observations, and an analysis of
documents or records. It could be any two or all three. One could
interview three people from different backgrounds on the same topic.
6. The advantage of using multiple data collection techniques is that the
researcher gets a broader or more in-depth view of a school or a situation.
Reality will reveal itself this way.
7. Data are interpreted without using mathematical analysis.
8. The study is attempting to address four concerns.
29

a. The study is concerned with things that a number cannot answer about
a school, such as spirit, atmosphere, great extra-curricular
activities, and educational quality.
b. Real-world situations are studied—without manipulations.
c. Specific questions are asked.
d. It is a rich detailed description.
9. The disadvantage is that the researcher may get too close to the people
being interviewed. This can bias a study.
10. It is important to have empathic neutrality—complete objectivity is
impossible. Try to stay neutral and objective. Try to define any potential
bias.
11. Five Key Things the Researcher Should Do
a. Pre-organize: Organize ahead of time the things that you need to do.
b. Collect the data.
c. Organize the data.
d. Interpret the data.
e. Write a report.
12. In qualitative research, the researcher is bringing reality to a study.
A qualitative study can supplement
A quantitative study, which will present
A better picture of reality and truth.
30

SUGGESTED STUDENT ACTIVITY:
1. Divide into groups of four to five students. As a group identify an area of
concern that you could develop a brief questionnaire to gather data.
(Examples could be: a) amount of additional fees charged to students at
registration; b) is recess beneficial to the academic development of
children? c) views on a policy issue in your graduate program, etc.)
Each member should write down five things they feel are important/their
views on the topic. Compare and contrast the viewpoints among the
group members. Are there patterns of concern or do you find a variety of
views on the topic.
2. Identify the steps needed to collect data on the topic discussed in activity
#1. What can each group member do to ensure they do not let their own
biases effect the collection of data? How could triangulation be used to
collect data on your group’s topic of interest?
31

Chapter 6
Methods and Tools of Research
Key Points
1. Qualities of a Good Test
a. Validity: A test is valid if it measures what it purports to measure.
b. Reliability: A test is reliable if it measures consistently over time.
c. A test can be reliable but still not be valid.
d. If a test is valid, it should be reliable and usually is reliable.
2. Types of Validity
a. Content: Questions should deal with content covered and the objective
taught.
b. Face: On the surface, it looks like a valid test or questionnaire.
c. Criterion: Two Types
1) Predictive: It can predict success in a certain criteria.
2) Concurrent: It is closely related to other measures.
d. Construct: Some other common measure is compared with the
construct.
32

3. Correlation Coefficient: The procedure quantifies the relationship of
paired variables.
Example:
-1 0 .7.8.9 1
These numbers indicate a high
correlation.
4. Buros Mental Measurements Yearbook can be helpful when you want to
compare Test A with Test B. It provides reviews of tests.
5. Helpful Suggestions for Constructing Your Own Test or
Questionnaire
a. Secure a panel of experts to assist you in constructing your questions,
such as professors of English and research.
b. Pilot the test or questionnaire. Administer it to ten to fifteen people
who will not be a part of your actual study. Score it and calculate
the Cronbach Alpha Coefficient for each of the test items to determine
reliability of the instrument.
c. Some time later, repeat the process of administering the test/
questionnaire to the same individuals, and again calculate the
Cronbach Alpha Coefficient.
d. The scores should be nearly the same. The correlation coefficient
should be high (a Cronbach Alpha of .62 or higher is considered
acceptable for social science research).
e. It would also be beneficial for you to ask teachers to provide
suggestions for improvement.
f. It is to your advantage to use a professionally prepared questionnaire.
Remember to get permission from the publisher.
6. Types of Reliability of Test or Questionnaire/Opinionnaire
33

a. Stability over time (test-retest): This is a very important aspect.
b. Stability over item samples: Equivalent or Parallel forms.
Example:
If there are 50 questions on a test or questionnaire, answer only the
odd numbered items. Score this part. Next, answer only the even
numbered items, and score this part. Your score should be very close
on each part. This is also true for different forms of a test.
c. Stability of items (internal validity): All test questions should have
commonality (similarly related).
⇒ Kuder-Richardson Test (KR 21): This is the average of all
possible correlations (of split halves).
d. Stability over scorers (inter-scorer): Scorers must be consistent in
scoring criteria. They must not be biased.
e. Stability over testers: Testers must be consistent in test administration.
f. Standard error of measurement: To determine the standard error of
measurement the scores will be put into a formula and calculated.
g. No test is totally reliable or valid.
h. If you have a valid test, it is probably reliable.
7. Characteristics of a Good Questionnaire
a. Covers a significant topic.
b. Looks important to respondent—State significance of topic.
c. Only seeks information that is not obtainable otherwise
d. Short as possible, clear and easy to complete
e. Attractive, neat, easy to duplicate.
f. Clear directions, define important terms
g. Avoid asking two questions in one item: Keep questions short and
concise.
34

h. Ask objective questions. Do not ask leading questions.
i. Questions should be presented from general to specific.
j. Avoid annoying, embarrassing questions.
k. If delicate questions are included, inform participants that all answers
will be kept anonymous. Code questionnaires to keep them
anonymous and to enable the researcher to identify which ones have
been submitted and which ones have not.
l. Easy to tabulate and analyze.
m. Computer tabulate, if possible.
8. Preparing the Questionnaire
a. Randomly mix subtest questions.
b. Give the questionnaire to friends to complete in order to obtain
feedback.
c. Pilot it in order to establish reliability.
d. Get permission from principal and superintendent to conduct research.
e. Include permission letter with the mailed questionnaire.
f. Include the following in the mail out:
1) Cover letter
2) Permission letter
3) Questionnaire
g. Inform participants that all information will be kept anonymous and
keep it anonymous.
h. Enclose a stamped, self-addressed return envelope.
i. Code the questionnaire for follow-up.
j. Inform participants the questionnaire is coded.
k. Scale to use.
35

Note: If one must use a scale, the Likert scale is the most common and
the most practical.
9. General Information Regarding Questionnaires
a. If you modify a questionnaire 25% or less, it is still valid. If you
modify it more than 25%, it is not valid.
b. To validate a questionnaire, get a group of professionals to review it.
c. When an instrument is reliable, it gets the same results over a period
of time.
d. A questionnaire must be reliable and valid.
e. To determine the reliability of a commercial test, the researcher should
write to the publisher of the test and request verification of test
validity. The publisher will provide this information to you. Buros
Mental Measurement Yearbook is available in university libraries.
This yearbook gives summaries of instruments.
SUGGESTED STUDENT ACTIVITY:
1) Continue your activity from chapter 5. Develop a questionnaire (8–10
questions) on your group’s topic of interest. Include only open-ended
questions on the questionnaire. (Other types of questions, other than
open-ended, might provide quantitative data instead of qualitative.)
Share this questionnaire with other groups in your class to determine if
questions are clear and easy to understand and answer. (Decide if data
will be collected through passing out a questionnaire or by a face-to-face
interview. REMEMBER, FOR THE RESULTS TO BE RELIABLE,
EACH QUESTIONNAIRE MUST BE ADMINISTERED WITH THE
SAME METHOD!)
2) Pass out your questionnaire or conduct a face-to-face interview to ask
other individuals outside your class to respond to your questions. As a
group, review the data you have collected. Look at the data gathered on
each of your questions. Look for main themes and concerns or ideas.
Interpret what the findings mean and how the results could be used to
make changes, keep the status quo, etc. Report your findings back to
your class.
36

Chapter 7
Descriptive Statistics and Normal Distribution
Key Points
1. The reason for statistics is that there are numerical data in educational
research. You will have to interpret, understand, and treat data.
2. Two Ways to Classify Numerical Data:
a. Non-parametric Data: Data that are not normally distributed
1) Nominal
a) Names or classifies someone or something
b) Examples
i. Social security numbers
ii. License plate numbers
iii. Bank account numbers
iv. Student identification numbers
c. Not very useful in research
2) Ordinal
a. Names, classifies, and ranks someone or something
b. Examples
i. Class rank
ii. Sports rank
b. Parametric Data: Data that assume normality
1) Interval
a. Names, classifies, ranks, and has equal intervals between
numbers
b. Has no true zero point
37

2) Ratio
a. Names, classifies, ranks, has equal intervals, and has a true
zero
b. Examples
i. Test scores
ii. Height of students
3. Descriptive Statistics: includes Measures of Central Tendencies and
Measures of Variability (also referred to as Spread, Dispersion, or
Scatter)
a. Measures of Central Tendencies
1) The mean is the arithmetic average.
i. The symbol for the mean is X .
∑X
ii. b. =X
N
iii. The mean indicates the arithmetic midpoint; it is the best
measure of centrality.
iv. Example:
2
4 4.8 = X
5 5 24.0
6 20
7 40
∑ X = 24 40
N = 5
X = 4.8
b. The median is the midpoint when the numbers are placed in an
ascending or descending order.
c. The mode is the number that occurs most often in a data set.
38

d. One purpose of the mean and median is to represent the “typical”
score.
e. When the distribution of scores is such that most scores are at one end
and there are relatively few at the other end (skewed distribution),
it is better to use the median because it is a better indicator of test
scores.
1) In a positively skewed distribution, the mean is pulled to the right
of the median.
2) In a negatively skewed distribution, the mean is pulled to the left
of the median.
4. Measures of Variability (may also be referred to as the Spread,
Dispersion, or Scatter)
a. Range: the highest number minus the lowest number
b. Sum of Squares: sum of squared units of deviation from the mean
1) Symbol: SS
(
2) Formula: X − X )2
c. Variance: the average squared units of deviation from the mean
1) Symbol
i. Sample: S 2
ii. Population: σ 2
2) Formulas:
2
−
(∑ X )2
i.
∑X
N
N
SS
ii.
N
39

iii. The variance is a value that describes the distance that scores
are dispersed or spread from the mean.
iv. This value is very useful in describing the characteristics of a
distribution.
d. Standard Deviation: average units of deviation from the mean
1) Symbol
i. Sample: S
ii. Population: σ
2) Formulas
i. σ 2
2
−
(∑ X )2
ii. ∑X
N
N
5. Normal Distribution (also referred to as Z Distribution, Z Theory, Normal
Curve, and Bell-Shaped Curve).
a. Characteristics of a Normal Curve:
1) It is symmetrical.
2) The mean, median, and mode are all at the same point – right down
the center.
3) The curve is the highest at the mean.
4) Most of the scores cluster or crowd around the mean and decrease
as they move away from the mean.
5) The curve theoretically never touches the baseline.
b. Some things in nature are close to being normally distributed, such as
the height of men and women, I.Q. test scores, and shoe sizes.
c. To get a normal distribution, sample size should be at least 32.
40

6. Normal curve
Percent of cases
under portions of
the normal curve
34.13% 34.13%
13.59% 13.59%
2.15% 2.15%
.12% .12%
(Standard Deviation)
-4 -3 -2 -1 0 1 2 3 4
68.26% Percentage of
frequencies in a
95.44% normal
distribution
99.74%
99.98%
Very few scores will extend above or fall below
three standard deviations from the mean.
7. Normal Distribution Percentiles
41

Percent of cases
under portions of
the normal curve
34.13% 34.13%
13.59% 13.59%
2.15% 2.15%
.12% .12%
(Standard Deviation)
-4 -3 -2 -1 0 1 2 3 4
.1% 2.3% 15.9% 50% 84.1% 97.7% 99.9% (Percentiles)
Very few scores will extend above or fall below
three standard deviations from the mean.
8. Two Ways of Computing Variance and Standard Deviation
a. Conceptual Way:
42

(raw ( SS ) (σ )
2
(σ )
score)
X (X − X ) ( X − X )2 SS
N
σ2
2 6 -4 16 8
4 6 -2 4 5 40 Square
of 8 = 2.8
6 6 0 0 40
8 6 +2 4
10 6 +4 16
∑ X =30 0 40
(
(Sum of Squares)
(
(Variance)
(Standard deviation)
Measures of Measures of
Central Tendencies Variability
X =6 SS = 40
Md = 6 σ2 =8
σ = 2.8
43

b. Computational Way
2 (∑ X )2 2 (∑ X )2
∑X − ∑X −
X X2 N N
N N
220 −
( 30) 2 ( 30) 2
2 4 220 −
5 5
5 5
4 16
6 36
8 64
10 100
∑ X = 30 220
900 900
220 − 220 −
X =6 5 5
5 5
6
5 30
220 − 180 220 − 180
N =5
5 5
40 40
=8
5 5
8 = 2.8
44

9. Correlation
a. Correlation is the linear relationship between two or more variables.
b. The degree of linear relationship is measured by correlation
coefficient.
1) The symbol is “r” for Pearson’s r. (Karl Pearson)
2) Types of correlation
i. Positive correlation
a) A perfect positive correlation is +1, which is rarely if
ever encountered.
b) Correlations of .7, .8, and .9 indicate a high positive
correlation.
c) Examples of positive correlation: As one increases, the
other has a tendency to increase.
⇒ high IQ and high GPA
⇒ height and shoe size
Example of a positive correlation – As scores in X
go up, scores in Y go up
Time spent Studying Grades on Test
X Y
John 1 2
Bob 2 4
Mark 3 6
Bill 4 8
Jeff 5 10
45

b. Negative correlation
1) A perfect negative correlation is -1, which is rarely if ever
encountered.
2) Examples of negative correlation: As one increases, the other has a
tendency to decrease.
⇒ Total oil production and price per barrel
⇒ More graduate courses taken in college and free time
Example of a negative correlation – As scores in X go
up, scores in Y
go down.
Time spent Studying Grades on Test
X Y
John 1 5
Bob 2 4
Mark 3 3
Bill 4 2
Jeff 5 1
3) iii. A negative correlation does not necessarily mean that a bad
situation exists. For example, a person who increases exercise
would likely lose weight.
c. No correlation
1) A perfect lack of correlation is zero; however, rarely would it fall
exactly on zero, such as in case of 1, .2, or .3
2) Examples of no correlation
⇒ Height and IQ
⇒ Total rice production and the price of gold
10. Three ways to Interpret Coefficient of Correlation (Pearson’s r)
46

a. .90 .80 .70 Rule
(high) (strong) (moderate)
1) .90 indicates a very strong relationship.
2) .80 indicates a strong relationship.
3) .70 indicates a moderate relationship.
4) .60 indicates a fair relationship.
5) Below .5 indicates that it may be due to chance.
6) There is a stronger indication that no relationship exists as the
number gets closer to zero, such as .2 and .3.
b. r2 = Coefficient of Determination: When the percent of X is known,
one could determine a percent of what Y would be.
An estimate of common variance between variables can be determined by
squaring the correlation coefficient.
1) Formulas
(∑ X ) (∑Y )
∑ XY −
r= N
∑ X 2 − (∑ X )  ∑Y 2 − (∑Y ) 
 2 2
 N  N 
  
↑ ↑
Sum of Squares Sum of Squares
of X of Y
(∑ X ) (∑Y )
∑ XY − N
r=
( SS X ) ( SSY )
47

2) Example
X X2 Y Y2 XY
John 1 1 2 4 2
Bob 2 4 2 4 4
Bill 3 9 3 9 9
Joe 4 16 4 16 16
Sam 5 25 5 25 25
∑ 15 55 16 58 56
56 − (15) (16)
r=
5
(10) ( 6.8)
SS X = ∑ X 2
−
(∑ X )2 = 55 −
15 2
= 55 −
225
= 55 − 45 = 10
N 5 5
SSY = ∑ Y 2
−
(∑Y ) 2
= 58 −
16 2
= 58 −
256
= 58 − 51.2 = 6.8
N 5 5
56 − 48
68
8
8.2
Pearson’s r = .97 (very high correlation)
48

X and Y have a lot in common.
r 2 = .94 (Given X, one could tell 94% of the time what Y would
be.
3) Coefficient of Determination: Given X, one could determine
94% of the time what Y would be.
4) Since correlation is concerned with prediction, it is more
difficult to predict the correlation as the correlation goes down.
c. t test: The test of the significance of the difference between two
means:
1) Think of a t-test as a correlation turned inside out.
2) A t-test indicates the difference between numbers, whereas a
correlation indicates the similarities between numbers.
11. Measures of relative position: standard scores
a. z score
1) When comparing scores in distributions where total points
may differ, a z score permits a realistic comparison of
scores and may allow equal weighting of the scores.
2) Formula
X−X
z=
σ
X = raw score
X = mean
σ = standard deviation
49

12. Normal Distribution Problems
Directions: Treat each of the following as if distribution is normal. What
percent of scores lies between the two z scores for each of the following
pairs?
(1) 3 and -3 ______ (5) 1 and -1 ______ (9) -.5 and 1.2 ______
(2) 0 and 1 ______ (6) 0 and .5 ______ (10) 1.3 and 2.4 ______
(3) 0 and 6 ______ (7) 1 and -2 ______ (11) 1.5 and -1.5 ______
(4) 2 and -2 ______ (8) 0 and -6 ______ (12) 0 and 2 ______
Directions: Treat each of the following as if distribution is normal.
Identify the z score for each of the following percentiles.
(13) 50th percentile ______ (19) 99th percentile ______
(14) 60th percentile ______ (20) 40th percentile ______
(15) 65th percentile ______ (21) 30th percentile ______
(16) 70th percentile ______ (22) 16th percentile ______
(17) 90th percentile ______ (23) 5th percentile ______
(18) 95th percentile ______ (24) 75th percentile ______
Directions: Treat each of the following as if distribution is normal.
Population mean is 32. Population standard deviation is 3.
Identify the z score for each of the following raw scores.
(25) 29 _____ (28) 35 ______
(26) 38 _____ (29) 26 ______
(27) 28 _____ (30) 33 ______
50

Directions: Treat each of the following as if distribution is normal. What
percent of scores lie between each of the following pairs of raw scores?
(population mean = 32 population standard deviation = 3)
(31) 32 and 35 ______ (36) 23 and 41 ______
(32) 29 and 26 ______ (37) 32 and 30 ______
(33) 38 and 41 ______ (38) 26 and 23 ______
(34) 32 and 33 ______ (39) 23 and 20 ______
(35) 35 and 38 ______ (40) 32 and 34 ______
SUGGESTED STUDENT ACTIVITIES:
Divide into groups of two to three students. USE YOUR CALCULATORS!
Use the following set of score to complete the following exercises:
63, 79, 88, 88, 87, 89, 89, 90, 90, 90, 93, 94, 95, 95, 98, 99
1. Compute the mean of the set of scores listed above.
2. Determine the median of this set of scores.
3. Does the mean differ from the median? Why or why not?
4. Find the range of this set of scores.
5. What is the mode of this set of scores?
6. Compute the variance of this set of scores.
7. Compute the standard deviation.
8. Using the mean and the standard deviation, plot these test scores to see
where they fall in a distribution around the mean.
9. Compare and contrast positive and negative correlation.
51

Chapter 8
Inferential Data Analysis
Key Points
1. Central Limit Theorem
a. The characteristics of sample means are detailed by this theorem.
b. Characteristics of sample means
1) Sample means are normally distributed.
2) The mean of sample means will be the mean of the population.
3) The sample means will have a mean (population mean) and a
standard deviation.
2. Null Hypothesis
a. A null hypothesis states that if there is a difference, it is due to chance.
b. By rejecting a null hypothesis, the researcher is providing a stronger
test of logic.
c. Additionally, by rejecting the null hypothesis, the researcher is
concluding there is a significant difference between the two means,
and this difference is not due solely to chance.
d. The .05 alpha level is often used as a standard for rejecting the null
hypothesis, which means that 95 times out of 100 the results are
not due to chance.
e. The .01 alpha level is a more rigorous test. It means that 99 times out
of 100, the results are not due to chance.
52

3. z test: One-tailed Test
a. One-tailed Test at the .05 alpha level.
b. A researcher thinks the scores of the sample will be superior to
established scores.
Acceptance Area
95%
Rejection Area
5%
X +1.65 (z score)
95% Acceptance Area
53

4. z test: Two-tailed Test at .05 alpha level
a. Two-tailed test at the .05 alpha level.
b. A researcher thinks the scores of the sample will be different from the
established scores.
Acceptance Area Acceptance Area
47.5% 47.5%
Rejection Area Rejection Area
2.5% 2.5%
-1.96 X +1.96
95% Acceptance Area
5. Critical value for z (rejection of null)
Test .05 alpha level .01 alpha level
One-tailed test 1.65 2.33
Two-tailed test 1.96 2.58
6. Degrees of Freedom
54

a. Definition: Conceptually, always N-1.
b. As the number of degrees of freedom increases, the strength of the
prediction increases.
7. Four Main Types of Tests Used in Educational Research
a. Independent t Test (very useful test)
1) Characteristics
2) No population mean
3) No σ
4) Compares the means of two different independent groups
5) Example
6) Group X has been taught with Method A; compute the mean.
7) Group Y has been taught with Method B; compute the mean.
8) The researcher wants to determine if one method is better than the
other method.
9) Formula for Independent t Test
X −Y
=

∑ X −
2 (∑ X )2  +  Y 2 − (∑Y )2 
 ∑ 
Independent t 
 N     N  
n ( n − 1)
X −Y
SS X + SSY
N ( N − 1) (Degrees of Freedom)
55

4. Used in medical, agricultural, and educational research
b. Correlated t Test (paired) (very useful test)
1) Characteristics
i. Pre and post tests (pairs)
ii. Only involves one group
iii. c. D = X −Y
2) Formula
X −Y
=
2
−
( ∑ D) 2
Correlated t ∑D
N
N −1
N
3. Example
a. Pretest each group then compute the mean.
b. Teach group using a special method. (The treatment)
c. Post test the group and then compute the mean.
d. The researcher wants to determine if there is a significant difference
between the pre- and post mean. If there is a significant difference,
then the special teaching method id helpful. (Null hypothesis is
rejected.)
56

c. Analysis of Variance (ANOVA)
1) The Independent t Test is a subset of ANOVA.
2) Characteristics
i. Involves three or more groups.
ii. All groups are treated differently.
3) Also referred to as the F Test, which was named after the man who
invented the test.
4) Formula
d. Pearson’s r (correlation)
1) Characteristics
i. Measures the degree of relation between two variables.
ii. Determines the degree of linear relationship between two
variables.
2) Formula
(∑ X ) (∑Y )
∑ XY −
N

∑ X −
2 (∑ X )2   Y 2 − (∑Y )2 
 ∑ 
 N  N 
  
57

Chapter 9
Parts of the Research Proposal
Note: The research proposal is a framework for any research study. A
proposal should also clearly and succinctly reveal your intended plan. In
most instances, university policy and specifications for the length of research
proposals are adopted; however, it is quality not quantity that is important
when writing a prospectus for research.
1. Title Page
a. Use enough descriptive words to catalog it by ERIC and Resources in
Education.
b. Example:
The Effects of Collective Negotiations on Teacher Job Satisfaction
in the Temecula School District in southern California.
2. Introduction to the Study
a. This part should be relatively short and capture the reader’s attention.
b. It describes what the study will cover and should be written in a
manner that will make the reader interested in the topic.
c. A brief background of where the study will be conducted may be
included.
d. The operative word for this section is “brief”. Keep in mind, this is a
proposal not the completed study.
3. Review of Literature
a. This component reviews pertinent literature and information relevant
to your topic.
b. Previous research should be included.
c. Five to 10 citations are satisfactory for the proposal.
58

d. Citations should be relevant and recent.
4. Statement of the Problem
a. This part logically establishes the different underlying intellectual
motives for conducting the research on this specific topic.
b. Opposing conclusions are a good way to set up the statement of the
problem.
c. Example: There appears to be opposing conclusions in the research
concerning collective bargaining and its effect upon the plight of
the teacher. Smith (2005) found that the bargaining had not benefited
teachers. Jones (2005) noted that bargaining had greatly enhanced
teacher morale.
5. Purpose of the Study
a. This section succinctly describes what the researcher intends to find.
b. Example: The purpose of this study is to determine the extent to
which the collective bargaining process has influenced teacher job
satisfaction levels.
6. Research Questions
a. In this part, you will break down the Purpose of the Study into several
pertinent research questions.
b. It is important for the following parts to fall logically in line:
1) Statement of the Problem
2) Purpose of the Study
3) Research Questions
c. Examples: What was the level of teacher job satisfaction before
bargaining rights? What was the level of teacher job satisfaction
after bargaining rights?
59

7. Hypotheses
a. The research questions are put in statistical terms in this section.
b. Example: There is no significant difference in teacher job satisfaction
following the acquisition of bargaining rights.
8. Definitions
a. In this part, define terms specific to your study that may not be
familiar to the outside reader.
b. Specifically define general terms the researcher assumes all
individuals would know but might be different in different school
districts in a state, region or nation.
c. Example: TAE-The school district affiliate of the National
Educational Association—Sixty-nine percent of all Temecula
School District teachers are members of this organization.
9. Assumptions
a. Any assumed aspect the researcher may take should be duly stated.
b. Example: The instrument used in this study will accurately measure
the job satisfaction levels of teachers.
10.Limitations
a. Any boundary or limitation of the study must be stated.
b. Example: The study will measure levels of teacher job satisfaction in
only one school district. Teachers surveyed may vary in years of
experience.
60

11. Methodology
a. This section includes the following four parts:
1) Subjects
i. Describe subjects or sample (who and where).
ii. The population may be described in this part.
2) Instrument
i. Give details about the test or instrument and specific
materials.
ii. Validity and reliability may be discussed.
3) Procedures
i. Describe a step-by-step process of the researcher’s plan of
action.
ii. The timeline and permission to conduct the study may be
included.
4) Data Analysis
i. Describe how the data will be analyzed.
ii. The following information should be included:
iii. The type of statistical test that will be used, whether or not
means will be compared, and whether or not charts or graphs
will be included.
12.Significance of the Study
a. State why this study is worthy of the time and effort that will go into
it.
b. Substantiate the reasoning behind conducting a study of this type in
this district, state or region.
c. Example: Data derived from this study will serve as a guide to school
districts in similar settings that are also considering the collective
bargaining process.
61

13.References
a. References should be relevant, recent, and cited in the American
Psychological Association (APA), Modern Language Association
(MLA), or any other required format.
b. A sufficient amount of references should be used. The number of
references will vary depending on the topic and resources
available.
SUGGESTED STUDENT ACTIVITIES:
1. Divide into groups of four to five students. Every group member should
contribute at least one area of concern that they would like to solve in
their role as educators. Identify one area of concern that is important to
the entire group. This will become the purpose of your study. Write three
to five research questions (what you want to know about the area of
concern).
2. Develop three to five hypotheses for your group study.
3. Define terms that may not be familiar to the outside reader that would be
related to your study.
4. Identify the methodology that would be used for your study. (Subjects,
instrument to be used to collect the data, procedures to be used to collect
the data, include a timeline of when this would be done, and the type of
statistical test you would use to analyze the data you will collect.)
62

Chapter 10
Parts of a Field Study
Note: Parts of the Field Study have been discussed in the section entitled
“Parts of a Research Proposal,” therefore only their titles will be listed
in this section. Additional parts and those parts that need to be
expanded will be listed and discussed in this section.
1. Title
2. Abstract
a. This is a summary of the complete study.
b. It is usually around a page in length.
3. Table of Contents
a. List the chapters of the study.
b. List only the page number on which each chapter begins.
4. Chapter 1: Introduction to the Study
a. This chapter includes the following parts:
1) Introduction to the Study
2) Statement of the Problem
3) Purpose of the Study
4) Research Questions and/or Hypotheses
5) Definitions
6) Assumptions
7) Limitations
8) Significance of the Study
b. This chapter is basically the proposal minus the Review of the
Literature and the Methodology.
63

5. Chapter 2: Review of the Literature
a. Expand the review of the literature.
b. Ten to twenty citations are sufficient.
c. Remember to keep the citations recent and relevant.
6. Chapter 3: Methods and Procedures
a. This is basically the part in the proposal that was labeled
Methodology. It will be expanded.
b. Describe in detail what was done in the study.
c. Some information in this section may have to be changed because the
information here will state what was actually done, not what the
researcher planned to do as was stated in the proposal.
7. Chapter 4: Analysis of Data or Results of Study
a. Describe in prose and in chart or graph form the numerical results of
the study.
b. Do not explain, summarize, or conclude in this chapter.
c. Tell and show only the results. Do not attempt to explain the results.
8. Chapter 5: Summary, Conclusion, and Recommendations
a. Summarize the results of the study.
b. An explanation may be given as to why the results turned out as they
did.
c. Try to consider all factors and variables that could have influenced
the dependent variable.
d. Recommendations for further study in regard to this topic should
be included.
e. Further study could likely be conducted on this issue at another
school or in a slightly different manner.
9. References
64

10. Appendices
a. Make a list of the location of specific tables, charts, or graphs.
b. Remember to include the chapter and page number.
A CHECKLIST OF ITEMS FOR TRADITIONAL FIVE CHAPTER
DISSERTATIONS & THESES
The following is a checklist of items which are typically included in a graduate research project,
thesis, or dissertation. Not all of the suggested categories are necessary or appropriate for all
studies, and the order of items within chapters may vary somewhat. These items are intended to
serve as a guide:
CHAPTER 1: INTRODUCTION
________ Introduction
________ Background of the problem (e.g., educational trends related to the problem, unresolved
issues, social concerns)
________ Statement of the problem (basic difficulty - area of concern, felt need)
________ Research Questions to be answered or investigated
________ Hypothesis or Hypotheses statements if needed or specified by advisor.
________ Purpose of the study (goal oriented) -emphasizing practical outcomes or products
________ Importance of the study - may overlap with the statement of problem situation
________ Assumptions (postulates)
________ Delimitations of the study (narrowing of focus)
________ Limitations of the study
________ Definition of terms (largely conceptual here; operational definitions may follow in
Methodology Chapter)
________ Organization of the Study....Outline of the remainder of the thesis or proposal in
narrative form.
CHAPTER II: REVIEW OF RELATED LITERATURE
________ Organization of the present chapter - overview
________ Historical background (if necessary)
________ USE KEY WORDS in each Research Question and follow with the literary review that
addresses each question.
Purposes to be Served by Review of Research Literature
________ Acquaint reader with existing studies relative to what has been found, who has done work,
when and where latest research studies were completed, and what approaches involving
research methodology, instrumentation, and statistical analyses: were followed (literature
review of methodology sometimes saved for chapter on methodology)
________ Establish possible need for study and likelihood for obtaining meaningful, relevant, and
significant results
________ Furnish from delineation of various theoretical positions, a conceptual framework affording
bases for generation of hypotheses and statement of their rationale (when appropriate)
________ Organize this chapter in the same order as the research questions are stated in chapter I. Be
very careful to fully align the review of literature with the research questions.
Note : In some highly theoretical studies the chapter "Review of Literature" may need to
precede "The Problem" chapter so that the theoretical framework is established for a
succinct statement of the research problem and hypotheses. In such a case, an advance
organizer in the form of a brief general statement of the purpose of the entire
investigation should come right at the beginning of the "Review of Literature" chapter.
65

Sources for Literature Review
________ General integrative reviews cited that relate to the problem situation or research problem such
as those found in Review of Educational Research, Encyclopedia of Educational Research, or
Psychological Bulletin.
________ Specific books, monographs, bulletins, reports, and research articles --- preference shown in
most instances for literature of the last ten year.
________ Unpublished materials (e.g.. dissertations. theses, papers presented at recent professional
meetings not yet in published form, but possibly available through another source.
________ Selection and arrangement of literature review often in terms of questions to be considered,
hypotheses set forth, or objectives or specific purposes delineated in problem chapter
________ Summary of literature reviewed ( very brief)
CHAPTER III: METHODOLOGY or the recipe/how to chapter
________ Overview or at least an introduction
________ Restate the research questions
________ Hypotheses stated in NULL FORM.
________ Description of research methodology or approach (e.g., experimental, quasi-experimental,
correlational, causal-comparitive, or survey)
________ Research design Spell out independent, dependent variables
________ Subjects of the Study (Clearly describe the sample and population.)
________ Instrumentation (tests, measures, observations, scales, and questionnaires)
________ Pilot studies (as they apply to the research design, development of instruments, data collection
techniques, and characteristics of the sample)
________ Validity--provide specifics on how you will establish validity or provide validity data specific
to your instrument from other studies with similar populations
________ Reliability--provide specifics on how you will establish reliability or provide data specific to
your instrument from other studies with similar populations
________ Procedures (Field, classroom or laboratory e.g., instructions to subjects and etc.
________ Data collection and recording
________ Data analysis (statistical analysis or qualitative analysis explained in detail)
________ Summary
CHAPTER IV : ANALYSIS OF DATA
________ Findings are presented in tables or charts when appropriate
________ Findings are reported with respect to furnishing evidence for each question asked
(ORGANIZED IN THE SAME ORDER AS HEADINGS IN CHAPTER I & III) or each
hypothesis posed.
________ Appropriate headings are established to correspond to each main question or hypothesis
considered
________ Other factual information kept separate from interpretation, inference, and evaluation (one
section for findings and one section for interpretation or discussion)
Note: In certain historical, case-study and other types of investigations, factual and
interpretive material may need to be interwoven to sustain interest level, although the
text should clearly reveal what is fact and what is interpretation.
________ Separate section often entitled "Discussion", "Interpretation", or "Evaluation" ties together
findings in relation to theory, review of literature, or rationale
________ Summary of chapter
CHAPTER V : SUMMARY, CONCLUSIONS, RECOMMENDATIONS
________ Brief summary of the study and findings portion of Chapter IV
________ Conclusions (Often restatement of the research questions key topics or variables and final
conclusions analyzing the answers)
66

________ Recommendations (practical suggestions for implementation of findings)
________ Recommendation for further study
ORGANIZATION AND STRUCTURE OF THE DOCUMENT
1. Copyright Page
2. Title Page
3. Signature Page
4. Abstract
5. Dedication Page
6. Acknowledgments
7. Table of Contents
8. List of Tables
9. List of Figures
10. Body text, divided into chapters designated by upper case Roman numerals
11. References in the specified style manual format
12. Appendices and supporting documents
13. Human Subjects Review Approval document
14. Author’s Vita
TABLES/FIGURES
1. Tables and/or figures should appear no more than one page from where they are first
referenced
2. Tables and/or figures may be placed in the appendices and referenced in the body text
3. Tables and/or figures are identified by chapter and number. ( Example: Table 4.1
would be first table to appear in chapter 4)
MARGIN SETTINGS:
1. 1 ½’ Left margin and 1” inch top, bottom and right margin or other university set
specifications
SPACING
1. Double spaced throughout the document
2. Indent each paragraph first line .05”
PAPER
1. 100 percent cotton, 20-pound bond
FONT AND SIZE
1. Arial, Bookman, Times New Roman or similar font recommended
2. Size: Standard 12 font
PAGINATION
1. Every Page should be assigned a number
2. Preliminary pages, small Arabic numbers (i, ii, iii, iv …etc) in the center at bottom of
each numbered page
3. Abstract receives the first numbering at the bottom and in the center
4. First page of each chapter should be in the center at the bottom of the page in the
footer
5. All other pages should have numbers in the upper right hand side of the page
67

Dissertation Web Resources:
http://www.dissertation.com This site has a number of great tips, feature articles and a
monthly newsletter related to the dissertation process.
http://www.jsmusic.org.uk/students/dissertations/dissertations_checklist.html This
site contains a valuable checklist for help with organizing and completing the document.
http://www.gradresources.org/worksheets/gantt.htm This site contain a neat chart
with each component and a timeline to help guide you through the steps to completion.
http://www.lib.duke.edu/libguide/plagiarism.htm This site defines and explains
plagiarism in detail along with the consequences for the act.
http://www.lib.duke.edu/libguide/home.htm Duke university provides a great resource
for selecting the topic and researching library resources on this quality website.
http://frontpage.wiu.edu/~rlm119/writinglinks.html Dr. Marshall’s writing site
contains a good set of links to assist with grammar, punctuation, style and other writing
issues.
http://frontpage.wiu.edu/~rlm119/apalinks.html Dr. Marshall’s APA site has a number
of good links to assist with APA in-text and reference list formatting.
http://www.citationmachine.net Citation machine is a good tool to utilize in the quest
for proper APA or MLA references.
http://frontpage.wiu.edu/~rlm119/templates.html Dr. Marshall’s template site should
save you some time in formatting table of contents and other essential pages of the
document.
http://www.academicladder.com/dissertation/dissertation-coaching-help.htm
Academic ladder provides a free bi-weekly tips subscription to help conquer some of the
problems and issues that arise in writing the dissertation or thesis.
68

Chapter 11
General Statistics Information
Key Points
1. Definitions of Statistics
a. Statistics involves manipulations of numbers and conclusions based
on these numbers.
b. Statistics means to state numbers.
c. Statistics is the study of numerical variation.
d. Statistics is making decisions with incomplete data (without having
all the numbers).
e. Statistics is a numerical characteristic of a sample.
2. Examples
a. Agricultural statistics (acres, grain, water, and fertilizer)
b. Medical statistics (types of drugs, amounts, and patients)
3. Two Types of Statistics
a. Descriptive Statistics
1) Summarizing or describing test scores (data) with numbers
2) Includes the mean, median, mode (Measures of Central
Tendencies)
b. Inferential Statistics
1) Definitions
i. A method of reaching conclusions about unmeasurable
populations using sample evidence and probability
69

ii. A method of taking chance factors into account when using
samples to reach conclusions about populations
2) Most research is done with a sample.
3) When a sample is selected, there is a certain level of uncertainty.
(A probability table is needed.)
4) Example
5 million 5th grade students (population) Teach using Method A
100 students
Teach using Method B
randomly selected
(sample of
above set)
Mean (average) for students taught using Method A = 48
Mean (average) for students taught using Method B = 52
(Students were taught differently.)
4. Population
a. Definition: Consists of all members (scores) of a specific group
b. The researcher selects his or her population. The following are
examples:
1) All fifth graders in the United States
2) All fifth graders in Texas
3) All fifth graders in Waller County
5. Sample
a. Definition: A subset of a population
b. Example
70

1) Of five million fifth grade students (population),
100 students were randomly selected (sample).
60 male 40 female
2)
students students
[Each is a sub sample of the above 1]
6. Parameter
a. Definitions
1) A numerical characteristic of a population
2) A statistic of a population
3) A measurement of a population
b. A constant
7. Statistic
a. Definitions
1) A numerical characteristic of a sample
2) A measurement of a sample
b. A variable
8. Experimental Design or Research Design
a. Definition: Concerned with all the things that influence the numbers
b. The way the researchers did their experiment may have influenced the
outcome.
c. Remember the definition of statistics – the manipulation of numbers
and the conclusion based on these numbers.
71

9. Variable
a. Definition: Something that exists in more than one amount or form
b. Examples
1) Height
2) Gender
3) Weight
4) Test scores
i. I. Q.
ii. IOWA
iii. LEAP
iv. ACT
10. Types of Variables
a. Independent variable: The treatment (selected by the researcher)
(IV)
b. Dependent variable: The observed results (in education, test
scores) (DV)
c. Extraneous variable: A variable other than the treatment (IV) that
might affect the results (DV)
d. Remember: IV (treatment) may or may not affect DV (results).
e. Examples of treatment
1) Different book
2) Different teaching method
3) Male/female teachers
4) Experience of teachers
5) Time of day
72

Chapter 12
Types of Statistical Data
Key Points
1. Nonparametric Data: Data not normally distributed (Non-normal) –
Discrete data -
a. Nominal Data (Refers to things)
1) Just names something or someone
2) Examples
i. Social security numbers
ii. Phone numbers
iii. I. D. number
iv. Credit card number
v. Home address
vi. Bank account number
3. Nominal data are not very useful in research. Averages can’t be
computed with this type of data.
b. Ordinal Data (Refers to frequency)
1) Names and ranks (ranked data)
2) Numbers tell you relative positions or orders
3) Examples
i. Class rank (1st, 2nd, 3rd, etc.)
ii. Rank by height
iii. Sports rank
iv. Rank in a contest
4) More useful than nominal but still not that useful
5) Not exact
73

6) Hides things
7) Intervals are not equal.
8) No math is involved
9) Ranking is not mathematical.
10) Can’t get an average rank
Example
Mrs. Smith thinks there is a correlation between how students rank in math
and science.
Mrs. Smith’s classes
_______________________________________________
Students Rank in Math Class Rank in Science Class
Mary 5 4
Joey 3 5
Alice 4 2
Sam 1 3
Bob 2 1
What does this “1” ranking really mean? We do not know how the class as a
whole performed. It could mean this student scored 60/100. That is why it is
maintained that ordinal data (ranking) hides information.
Instead of ranking, Mrs. Smith should use the actual test scores of students
because they are more specific data.
It is best not to use stanines either when comparing students.
1 2 3 4 5 6 7 8 9
Bottom
top
4% 7% 12% 17% 20% 17% 12% 7% 4%
74

2. Parametric Data: Data that are normal. (Continuous)
1) Interval Data
2) Names, ranks, and has equal intervals between numbers
3) Example: Temperature (i.e. Fahrenheit)
4) Cant get good mathematical data
5) Cant get a mathematical average
6) Has equal units of measurement
7) Many educational and psychological studies have been done using
interval data.
b. Ratio Data
1) Names, ranks, has equal intervals, and has a true zero point
2) Examples
i. Height
ii. Time
iii. Distance
iv. Some test scores (i.e. a teacher’s test)
v. Speed
vi. Weight
vii. Income
3) Can compute mathematical operations
4) Can get an average
5) Can say something/someone is twice, three times, etc. as tall, fast,
heavy, etc.
75

Scales of Different Types of Data
Nonparametric Data (non-normal) (discrete data – just there)
1. Nominal
2. Ordinal
Parametric Data (assumes normality) (continuous)
3. Interval Mathematical operations can be
4. Ratio computed with these types of data.
½ of the scores ½ of the scores
average
As you move farther from the average, the percentage gets smaller.
76

77

Chapter 13
Descriptive Statistics
(Summarize or describe test scores)
1. Two Types of Descriptive Statistics
a. Three Measures of Control Tendencies
1) Mean: arithmetic average
2) Median: midpoint in a distribution of scores arranged in ascending
or descending order
3) Mode: the number in a data set that occurs most often
4) Examples
i. X
78

m
middle score (no score occurs more
than any other)
bimodal trimodal
30
Summation of
ii. Y
79

T
To obtain the median,
Note: To obtain the median, find
t
take the average of
the average of the two middle
t
the two middle
numbers, 5 and 6.
numbers
n
5.5 = median
+6 2 11.0
49 10
10
Summation of 10
1
80
7

2. The mean is a mathematical entity because the operations involved in
computing it are addition, multiplication, and division.
3. Types of Distribution
a. Normal Distribution
b. Positively Skewed Distribution
c. Negatively Skewed Distribution
4. Characteristics of a Normal Distribution
a. The bell curve is symmetrical.
b. The highest point is the mean.
c. The mean, median, and mode are located at the same place on the Bell
curve.
d. The mean, median, and mode are located at the 50th percentile.
e. The scores cluster around the mean. As you move farther to the left or
right, there are fewer and fewer scores.
f. Half of the scores are above the mean, and half of the scores are
below it.
g. Most people score around the mean.
h. The curve never touches the baseline and goes forever in both
directions because it is a theoretical model.
i. Example
57 58 58 59 59 59 60 60 60 60 61 61 61 62 62 63
81

on either side of 60; therefore, the
The same amount of numbers are
mean, median, and mode are
located at the same place.
Distribution
Normal
In a curve distribution, the slope represents the frequency of
score
When thousands of people are involved, scores tend to fall into a
normal curve.
5. Characteristics of a Positively Skewed Distribution
a. The mean, median, and mode are not located at the same point.
b. Outliners cause distortion and cause the mean to be pulled to the right.
c. When the mean is pulled to the right, you have a positively skewed
distribution.
d. The mean is higher than the median.
e. Example
57 58 58 59 59 60 60 60 61 61 62 69
The median is 60, however 69 is the outliner and causes the mean to be
greater than the median.
82

6. Characteristics of a Negatively Skewed Distribution
a. The mean is to the left of the median.
b. The mean is lower than the median.
6) Example
50 59 59 60 60 60 61 61 62
The median is 60, however 50 is the outliner and causes the mean to be
lower than the median.
7. Facts to Remember
a. In a skewed distribution, the best indicator is the median because it
does not move.
b. When the mean is more than the median, it is a positive distribution.
c. When the mean is less than the median, it is a negative distribution.
8. The median is always the center.
9. The mean can be pulled to the right or left.
10. In skewed distributions, use the median to report a class average.
Measures of Variability (also called Spread, Scatter, Dispersion, and
Deviation)
1) Range: the highest score minus the lowest score
a. If the range is small, the standard deviation will also be small.
b. If the range is large, the standard deviation will also be large.
2)Sum of Squares: The sum of the squared units of deviation from the
mean; the central mathematical point from
which everything in parametric statistics is
based around
83

3) Variance: the average squared units of deviation from the mean
4) Standard Deviation: the average units of deviation from the mean
5) Symbols for…
Population Sample
SS SS
σ2 S2
σ S
for Mean
Population Sample
µ X or F or Y or Z or MSU
(anything with a bar over it)
Note: Always ask if you are computing the standard deviation for a
population or a sample. The formula is slightly different.
Conceptual Way (slow way)
Raw Deviation Sum of Variance Standard Deviation
Scores Mean from Mean Scores (for population) (for population)
84

Computational Way (fast way)
(
Raw Variance Standard Deviation
Scores (
(for population and sample) (for population)
2
−
(∑ X )2
Sum of Squares: ∑X
N
N
2
−
(∑ X )2
Variance: ∑X
N
N
(for a population) (for a sample)
2 ( ∑ X )2 2
−
(∑ X )2
∑X − ∑X
Standard Deviation: N N
N N −1
Central Measures and Variability
Directions: Find all central measures (mean, median, and mode) of all
distributions.
Find all measures of variability (sum of squares, variance,
and standard deviation) of distributions.
1) 2) 3) 4)
11 12 13 14
85

11 12 13 14
12 13 14 15
13 20 16 15
13 20 17 18
13 23 18 18
14
5) 6) 7) 8)
3 3 3 3
3 6 9 8
3 9 12 11
4 12 12 12
4 15 12 12
5 15 12 13
9) 10) 11)
2 4 1
4 4 1
6 4 10
86

Chapter 14
Types of Distributions
Key Points
1. Mesakurtic Distribution
a. This is a normal distribution.
b. The curve is symmetrical.
c. Example:
34.13% 34.13%
13.59% 13.59%
2.15% 2.15%
.12% .12%
(Standard Deviation)
-4 -3 -2 -1 0 1 2 3 4
.1% 2.3% 15.9% 50% 84.1% 97.7% 99.9%
2. Platykurtic Distribution
87

a. This distribution is basically flat.
b. It has the most variability.
c. Example:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
3. Leptokurtic Distribution
a. Practically everybody scores in the middle.
b. This type of distribution has the least variability.
c. There is no trend. (The trend is there is no trend.)
5
5
5
5
5
2 3 4 5 67
KURTOSIS IS THE TERM USED TO DESCRIBE THE
VARIABILITY (SPREAD) OF A DISTRIBUTION.
88

Chapter 15
FORMULAS
Name of Test Characteristics Formula
 one sample
 based on
normal X −M
z distribution z0 =
σ
 σ (population
standard
N
deviation) is
known
 critical z is
always 1.65
at .05 alpha
level
X −M
 one or two t0 =
S
tailed
N
t  σ is
unknown DF = N − 1
 one sample
X −Y
Independent  two different
 2  2
t independent  ∑ X 2 − ( ∑ X )  +  ∑Y 2 − ( ∑Y ) 
Test groups  N   N 
   
 no σ
 no population n ( n − 1)
mean DF = n1 + n2 − 2
89

Name of Test Characteristics Formula
 pre and post X −Y
Correlated tests (pairs) t=
t  same group 2
−
( ∑ D) 2
∑D
Test  D=x− y N
N −1
N
DF = N − 1
 measures the
degree of ( ∑ X ) ( ∑Y )
Pearson’s r relation ∑ XY − N
(Correlation) between two
 ∑ X 2 − ( ∑ X )   ∑Y 2 − ( ∑Y ) 
 2  2
variables
 determines  N  N 
  
the degree of
linear
relations
Chapter 16
90

The Basics Understanding and Using Statistics
1. The most common skill necessary for doing statistics is counting. For
example:
a.the number of days a student is present or absent
b.the number of items correct or incorrect on a test
c.the number of discipline referrals
d.frequency of unacceptable or desirable behaviors
e.the number of attempts required to master a skill
2. The second most common skill used in statistics is measurement. For
example, things we measure in education include:
a.achievement of individuals or achievement gaps between groups
b.aptitude
c.interest
d.skill level
e.knowledge
f.attitudes of teachers, parents, students toward specific thing
g.opinions of various constituencies
h.beliefs of important players in the organization
i.level and type of motivation
j.degree of improvement
k.progress
l.behaviors
3. The most frequently applied mathematical operations in statistics include
addition, subtraction, multiplication, and division.
If you know how to count, measure, add, subtract, multiply, and divide,
91

then you ALREADY possess the skills necessary to do statistics.
4. Many statistical concepts have become a part of our daily vocabulary.
We use these concepts without thinking. For example:
a. I am going to calculate the “average.” (statisticians call this the
arithmetic mean or mean)
b. She is above average. (statisticians say more precisely that her
performance on a measurement was one, two or three standard
deviations above the mean.)
c. I am 99.9% sure. (statisticians call this p < .001 or confidence level;
that is to say, these results were not due to accident or chance)
d. That information seems a bit “skewed.” (statisticians say that the
mean and median are not equal and that the distribution is positively
or negatively skewed)
e. There is a correlation between this and that. (statisticians say that
there is a statistically significant relationship between this and that.
The correlation is usually stated in numeric form, for example r=.34,
p< .01)
5. Established research designs and procedures for calculating and thinking
about statistics already exist. All you have to do is learn the directions and
follow them. Making your easier are the facts that:
a. Research design tells you what data to gather.
b. Statistical procedures and formula already exist and can be used
for calculating your data.
c. Statistical software such as the Statistical Package for Social
Sciences (S.P.S.S.) and S.A.S. make the analysis of your data very
systematic and complete including tables, graphs and charts.
1) SPSS is a quality software application for students in the initial
stage of learning statistical analyses. In addition, SPSS is a low
92

cost resources to students and it provides professional statistical
analysis and tools in a user friendly software environment for
both MAC and PC users. A list of resources for learning SPSS is
provided at the end of the chapter.
2) SAS is a more complex package with high levels of statistical
analysis capabilities. SAS handles a wide variety of specialized
functions for data analysis and procedures. This software
package is utilized extensively in business, industry as well as
educational settings. tools for both specialized and enterprise-
wide analytical needs. SAS is provided for PC, UNIX, and
mainframe computer platforms. A list of resources for learning
SAS is provided at the end of the chapter.
6. In a very short time you will realize that you can use your existing skills
but will use them MORE skillfully when you do statistics.
a. By counting, measuring, comparing, and examining relationships
of the RIGHT things you will be able to skillfully analyze data
and draw accurate and MEANINGFUL conclusions.
b. You will learn to use your findings and conclusions to make better
informed educational decisions.
93

Web Resources for SPSS
• http://www.utexas.edu/its/rc/tutorials/stat/spss/spss1/index.html
• http://www.ats.ucla.edu/STAT/mult_pkg/whatstat/default.htm
• http://www.stat.tamu.edu/spss.php
• http://www.spsstools.net/spss.htm
• http://cs.furman.edu/rushing/mellonj/spss1.htm
• http://www.ats.ucla.edu/stat/spss/examples/default.htm
• http://www.psych.utoronto.ca/courses/c1/spss/toc.htm
• http://www.ats.ucla.edu/stat/spss/modules/default.htm
• http://data.fas.harvard.edu/projects/SPSS_Tutorial/spsstut.shtml
• http://www.cas.lancs.ac.uk/short_courses/intro_spss.html
• http://www.cas.lancs.ac.uk/short_courses/notes/intro_spss/session1.pdf
• http://www.bris.ac.uk/is/learning/documentation/spss-t2/spss-t2.pdf
• http://calcnet.mth.cmich.edu/org/spss/toc.htm
• http://www.indiana.edu/~statmath/stat/spss/
• http://dl.lib.brown.edu/gateway/ssds/SPSS%202%20Hypothesis%20Testing
%20and%20Inferential%20Statistics.pdf
• http://dl.lib.brown.edu/gateway/ssds/SPSS1%20Finding%20and
%20Managing%20Data%20for%20the%20Social%20Sciences.pdf
• http://www.shef.ac.uk/scharr/spss/index2.htm
Web Resources for SAS
• http://www.itc.virginia.edu/research/sas/training/v8/
• http://www.ats.ucla.edu/stat/sas/sk/
• http://www.ssc.wisc.edu/sscc/pubs/stat.htm
• http://web.fccj.org/~jtrifile/SAS2.html
• http://www.utexas.edu/cc/stat/tutorials/sas8/sas8.html
• http://www.ats.ucla.edu/stat/sas/modules/
• http://www.psych.yorku.ca/lab/sas/
• http://instruct.uwo.ca/sociology/300a/SASintro.htm
• http://web.utk.edu/~leon/jmp/
• http://www.stat.unc.edu/students/owzar/stat101.html
94

Chapter 17
Getting Started With Research: Avoiding the Pitfalls
Any of the following mistakes can prevent a study from getting off the
ground or being carried out to completion. Avoid these mistakes by listening
to the voice of experienced professors when they tell you to modify your
study. Consider the following mistakes and the proposed solutions.
1. Research is conducted with conflicting purposes or research questions that
do not match your stated purpose. Research efforts may halt due to the
confusion.
Solution: Write the purpose and research questions with clarity and
simplicity. Allow expert writers to critique your work and take their
suggestions seriously.
2. Researcher fails to distinguish between the practical problem and the
research problem. She may try to save the whales with her study when a
better understanding of the problems that endanger the whales is needed.
The study may prove too unwieldy to complete. The goal is may be too
grandiose to be unattainable.
Solution: Map out the entire research agenda necessary to address a
practical problem then carefully carve out for your own study the part
that is most significant and workable. Remember that your goal is to
finish.
3. Researcher attempts to make the study overly complex when a simpler
design would yield equally useful information. The study may become
unwieldy and may obfuscate rather than shed light on the subject.
Solution: Examine all research questions included in your study and rank
them in order of the significance and usefulness. If any data do not help
fulfill the purpose of your study, then these should be dropped so that the
other areas can stand out.
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4. Researcher attempts to define the problem and purpose of the study
without first engaging in an extensive reading of all relevant literature.
This results in a superficial or naïve study that is not very useful.
Solution: Read everything you can get your hands on systematically sort
the types of studies and conceptual areas. Your study will take on a well-
informed vision of what more needs to be known.
5. Researcher defines the problem and purpose of the study without first
seeking the counsel of experts who are knowledgeable about the subject.
Once completed, the study may lack credibility with practitioners.
Solution: Spend a great deal of time talking to practitioners about the
problems they face when dealing with the issues that you are interested in
writing about. Let them provide you with an expert perspective as you
seek to define the problem and purpose of your study.
6. Researcher uses methodologies that he does not understand well. If the
design is inappropriate to the purpose of the study or the form of the data is
wrong, he may be unable to interpret the data or complete the study.
Solution: Consult statistics and research design experts regarding your
goals as a researcher. Take courses that you need to become proficient in
the specific methodologies that you wish to apply to your study.
7. The methodology or the title of the study drives the study rather than the
purpose. When a study driven primarily by methodology, the purpose and
significance are diminished to make the study easier to complete. This
may result in a less significant or useful study.
Solution: Do not title your work until you understand the research
problem well and the purpose that your study will reflect. Avoid selecting
a cool sounding methodology until you are certain that there it will help
you answer the specific things that you need to know.
8. Catchy phrases or terms are used to define the purpose and problem while
little attention is paid to the significance of a study. Study may be well
done, or even interesting, but may not be very useful.
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Solution: The significance of a study can mean the difference in whether
the study is published or whether it actually is read. Understand who the
intended audience of a study may be and try to address their interests and
needs and particularly what they need to know.
9. Study is not sufficiently delineated and limited so that the time or effort
required to complete the study becomes overwhelming.
Solution: Listen to your professors when they tell you the study may take
a lot longer if it is not narrowed down. Provide a “recommendations for
further research” section in your work so that extraneous matters may be
addressed in the future by you or other researchers.
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Chapter 18
Ethics and Research
1. Responsible conduct guiding researchers. Universities, federal and state
government as well as professional organizations have guidelines on
ethical behavior and research.
2. Informed consent - Participants must be informed and voluntarily give
their consent to participate in a study.
- Participants must be fully informed about all procedures and possible
risks.
- Participants informed of purpose of research and how data will be used.
- Benefits of study.
- Alternative treatments and potential compensation.
- They must understand and arrive at a decision without coercion.
(Voluntary participation)
- Starts before the research begins.
- Privacy and confidentiality of research subjects and data .
- Contacts
- Approval of the IRB (Internal Review Board)
3. Termination of research if harm is likely. Risk-benefit assessments.
4. Special protection for vulnerable populations of research subjects.
5. Equitable recruitment of participants.
6. Results should be for the good of society and unattainable by any other
means.
7. Beneficence - To promote understanding and shed light on the human
condition. Protection of those participating in the study.
8. Honesty - No data to be suppressed, data should be reported as collected.
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9. Misconduct
- Fabrication
- Falsification
- Plagiarism
SUGGESTED STUDENT ACTIVITIES:
1. In small groups discuss the relationship between academic freedom and
research ethics. Share your discussion with the entire class.
2. What steps should researchers take to ensure all areas of informed consent
are addressed in their research study? Share your discussion with the class.
3. What steps would you take to make sure you are not involved in unethical
conduct in research? Share your discussion with the class.
WEBSITES
APA's Research “Ethics and Regulation”
http://www.apa.org/science/research.html
National Institutes of Health (NIH) “Bioethics Resources”
http://www.nih.gov/sigs/bioethics/index.html
Research Ethics
http://faculty.ncwc.edu/toconnor/308/308lect10.htm
The National Institutes of Health (NIH) "Human Participants Protections
Education for Research Teams”
http://ethics.od.nih.gov/
The Department of Health and Human Services' (DHHS) Office of Research
Integrity http://www.ori.hhs.gov/
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Chapter 19
Ethics in Research on Human Subjects and the
Role of the Institutional Review Board
Frequently Asked Questions
1. What is an IRB?
The IRB is a committee that is assigned the task of reviewing
proposed research by a university or other institution that receives
federal funds and is in the business of conducting research on human
subjects. The IRB is required by part 46 of Title 45 of the Code of
Federal Regulations also called 45 CFR 46. According to the
Department of Health and Human Services, it is the responsibility of
the IRB to recommend to university officials that proposed research
either be approved or disapproved based on a set of rules called the
Common Rule.
2. Why do we have IRBs?
Every institution that conducts research on human subjects that also
receives federal funds must provided a formal mechanism for
ensuring that research is conducted in a manner that reflects nationally
recognized standards. Failure to comply with policy can place the
researcher and his institution at risk for litigation. In some a few
instances the federal government has temporarily suspended all
research activities at key research universities for failure to comply
with the law.
3. What is the Common Rule
The Common Rule was established in 1991 in federal law 45 CFR
46.112. It details all of the areas of compliance with accepted norms
for conducting research on human subjects established by the Helsinki
Agreement and a series of declarations referred to as the Belmont
Report. These principles re detailed in the Common Rule. These
include:
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a. informed consent
b. protection of confidentiality or anonymity of all human subjects
c. acknowledging the right of the subject not to participate in a study
d. ensuring that subject is aware of his or her right to discontinue the
study at any time without adverse consequence
e. ensuring that the study provides a benefit to the community
f. ensuring that the study has a direct benefit for the subject
participating in the study
g. ensuring that the subject is aware of the risks involved in the study
h. ensuring that the researcher has found less invasive or intrusive
ways to obtain the same information
i. that the individual subject has given permission to be deceived
during an experimental study
j. that parents have granted permission for children under the age of
18 to participate
k. that any psychological or physical harms will be remedied with
expenses paid by the researchers.
l. the researcher is protected from possible harms or is taking
informed risks
m. specific measures for achieving each of the above has been spelled
out
n. that theses measures are meticulously followed.
4. Are all studies subject to IRB approval?
No. However all studies that will involve gathering data from the public
or that will be published in some form must be reviewed before
university officials will approve the protocol. To accommodate social
science research and historical research expedited review protocols are
submitted. Studies that must be reviewed meet the following criteria:
a. the results will be published
b. the study involves experimentation on human subjects
c. the study is invasive or intrusive in some way
d. the study involves deception
e. there are possible risks to the subject
f. there may be no community benefit or direct benefit for the
subject
g. there is a possible conflict of interest by researchers in the study
h. medical or mental health research
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5. When my study has been approved by the IRB, are there any additional
requirements that researchers must follow?
Yes. The Common Rule states that research approved by an IRB may be
subject to further review for approval or disapproval by officials of the
institution under the following circumstances:
a. if a third party complains of possible wrong-doing or harms
realized
b. a senior administrator at the university may raise questions that
would result in a follow-up IRB review.
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Chapter 20
Working with the IRB
Suggested Frame of Mind for Researchers
The following suggestions are based on the assumption that the researcher
and the Institutional Research Board (IRB) regulator find themselves on
common ground – partners in learning to cooperative in improving research
and its ethical oversight.
1. Become an expert in the ethical issues surrounding your specific
research purpose, related questions, and methodology. Not all studies
require the same degree of IRB monitoring.
2. Become an expert in the ethical standards for research in your academic
discipline. Carefully worded research proposals may allow IRB
regulators to approve it without incident.
3. Become an expert in the IRB process of your institution. Examine how
each part of the IRB protocol or checklist relates to the ethical issue of
your particular study, methodology, and academic discipline.
4. Get to know your IRB members personally. Don’t wait until you
submit your proposal or go to the IRB meeting to discover who they
are.
5. Assume that IRB members want to do a good job. Empathize with them
as you would someone who is in training for a new job.
6. Continue to conduct occasional conversations with IRB members after
your proposal has been approved. Over time IRB members will come
to view your research proposals with greater confidence.
7. Before IRB meetings listen carefully to IRB members talk to you about
research and ethics. Be prepared in non-public, non-confrontational
ways to share your concerns regarding their statements or written
comments.
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These guidelines can help you get off to a good start without cynicism or
frustration. A positive working relationship with the IRB can promote good
professional health within your research community.
IRB RESOURCES ON THE WEB:
http://en.wikipedia.org/wiki/Institutional_Review_Board This sited defines
the purpose and premises for ethics in research along with the basis for reviewing
and monitoring behavioral research involving human subjects.
http://www.irbforum.org/ This site provides support and a forum for discussing
ethical, regulatory and policy issues related to human subjects research
http://www.northshorelij.com/body.cfm?id=5545&plinkID=5096 This site
provides a good IRB Map to assist in decision regarding submission of an IRB.
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Chapter 21
RESEARCH, WRITING & PUBLICATION
1. Brainstorm ideas for research and possible publication.
- Look at current journals to see what is current or a “hot” topic. Many
also have a “Call for Papers” listing the topics they plan to publish in
future editions.
- Ask professional educational organizations what topics are popular or
important issues in their field of education.
- Think about what interests you. You have to live with the topic until
you complete it. If you are not interested in the topic, it will become
boring or be difficult to keep on task and complete.
- Find out if a colleague or another person in the field of education has a
project, interest, etc. that you could work on with them.
- Find out if a textbook company is looking for someone to write a
chapter in a textbook. These might be on their website or they might
send an email to those on their listserve.
2. Determine the type of manuscript you want to write. (NOTE: You are
working on a manuscript. Many people call or interchange the term
article for manuscript. A MANUSCRIPT is work that is submitted for
possible publication. An ARTICLE is a manuscript that has been
published.)
- Objective survey of the literature available on a topic
- Analysis of literature to support the author’s viewpoint
- Interpretive paper on a specific theory, concept, etc.
- Theory paper that develops a new conceptual framework
- Research paper - describing the study, participants, results,
conclusions, etc.
- Chapter for a textbook (They are the easiest to be accepted since they
do not have to go through a blind peer-review process)
- Other types of papers as indicated in the professional journals you read
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3. It's also important to know what types of manuscripts a journal typically
publishes.
- The library should have current issues for your review. Many can be
found online.
- Review the types of article in several issues of the journal. Do they accept
a variety of topics for publication or do they have a theme for the issue?
- Read the submission or author guidelines. Many can be found online.
- Look at the expertise of the members of the editorial board for ideas
on their research interests.
4. The acceptance rates of journals can range from 80% to 5%. Look at
publishing in journals where the turnaround time may be shorter.
Journals which have very high submission rates have high rejection rates.
Look at using your time wisely. Don’t “tie up” an article for 18 months
if the journal has a low acceptance rate.
5. Ask colleagues which journals they have submitted manuscripts to. They
can give good advice on the “where to” and “where not to” for submissions.
6. Determine which journal you will submit your manuscript. It is
important to know where you are going to know how to begin the writing
process. It is like taking a trip. You can have a well organized vacation
by using a map or a “fly by the seat of your pants” experience without the
map. You save time, energy and have a greater chance for successful
publication by knowing where you are going. (Remember research
ethics. Only submit your manuscript to one journal at a time. You can
submit to another journal if you receive notice that your manuscript will
not be published by the editor.)
7. When possible, collaborate in writing! A group of two or more can share
ideas and the work.
- Decide on the topic
- Decide the role and responsibility of each team member. (Use each
other’s talents. Some are better at writing, others at finding the
references, others at editing, etc.)
- Set timelines
- Meet on a regular basis to keep each other on task, and make changes
as needed.
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8. Schedule a time to write every day. Make it automatic! Thirty to ninety
minutes a day, or at least three times a week. This will help you to stay
on target and not get overwhelmed at the last minute when your writing
project is due.
9. Develop an outline for your manuscript. You can read the published
articles in the journal where you plan to submit and determine what type
of outline to develop.
10.Write your introduction and summary first. Most problems are found in
these sections. They become a guide to your manuscript (a roadmap)! It
will keep you focused on the route you are taking.
11.As you write make sure the manuscript indicate you know what is current
on that topic. Make sure to have at least one to two references from the
same year you plan to submit your manuscript.
12.Make sure your manuscript has a solid conceptual basis.
13.Make sure that findings in your conclusion have been substantiated in
your paper.
14.When the paper is well organized and near completion have a couple of
colleagues review and edit it.
- Does it make sense to someone else who has read it?
- Does it follow the publication style? (APA, Chicago, MLA, etc.)
15.Tips for submitting your manuscript after it is completed:
- Make sure you have the exact copies required.
- Write a cover letter with the current editor’s name.
- The cover letter should be neat and a brief description of your
manuscript, why you are submitting it and your contact information.
- If an online submission, are all guidelines for submission followed?
- If mailing the manuscript, make sure you have the post office weigh the
envelope so you can buy the correct amount for postage.
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16.Most editors will document they have received your manuscript through a
letter or email. If you do not receive a letter within a couple of weeks
documenting that your manuscript was received then call or email the
editor to check to see if the manuscript was received. Remember FedEX
trucks and mail trucks have crashed and hurricanes have damaged mail.
Sometimes forces of nature and accidents do cause a manuscript to fall by
the wayside.
17.If you get an acceptance letter, GREAT JOB!! If you receive a letter
indicating the manuscript was not accepted for publication. review the
editorial comments.
- Revise and resubmit if the editor indicates this should be done.
- If you have questions about the comments made by reviews, contact the
editor and ask them for clarification.
- Ask the editor if they have a suggestion for another journal that might be
more appropriate.
- Revise and look at other potential journals for possible publication.
- Don’t worry, your manuscript might not have been the “right fit” for that
journal or the right time to be submitted there.
- Sometimes a journal receives several manuscripts on the same topic. The
topic might be saturated. Look for another journal to submit the
manuscript.
- Take heart that everyone will get some “rejection” letters. One of your
authors had that experience four times on her first manuscript. Although I
kept writing other manuscripts and those were being accepted, the first
one was rejected four times. On the fifth submission it was published.
NEVER GIVE UP, JUST KEEP SEARCHING FOR THE RIGHT
JOURNAL.
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PART II:
Fundamental Terms in
Educational Research
and Basic Statistics
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Fundamental Terms in Educational Research
and Basic Statistics
A priori codes – codes developed before examining the current data
A-B-A design – a single-case experimental design in which the response to
the experimental treatment condition is compared to baseline responses
taken before and after administering the treatment condition
A-B-A-B design – an A-B-A design that is extended to include the
reintroduction of the treatment condition
Accessible population – the research participants available for participation
in the research
Achievement tests – tests designed to measure the degree of learning that
has taken place after being exposed to a specific learning experience
Acquiescence response set – tendency to either agree or to disagree
Action research – applied research focused on solving practitioner’s
problems
Alternative hypothesis – statement that the population parameter is some
value other than the value stated by the null hypothesis
Amount technique – manipulating the independent variable by giving the
various comparison groups different amounts of the independent variable.
Analysis of covariance – used to examine the relationship between one
categorical independent variable and one quantitative dependent variable
controlling for one or more extraneous variables; it’s a statistical method that
can be used to statistically “equate” groups that differ on a pretest or some
other variable
Analysis of variance – see one-way analysis of variance
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Anchor – a written descriptor for a point on a rating scale
Anonymity – keeping the identity of the participant from everyone,
including the researcher
Applied research – research about practical questions
Aptitude tests – tests that focus on information acquired through the
informal learning that goes on in life
Archived research data – data originally used for research purposes and
then stored
Axial coding – the second stage in grounded theory data analysis
Back stage behavior – what people say and do only with their closest
friends
Bar graph – a graph that uses vertical bars to represent the data
Baseline – the behavior of the participant prior to the administration of a
treatment condition
Basic research – research about fundamental processes
Boolean operators – words used to create logical combinations
Bracket – to suspend your preconceptions or learned feelings about a
phenomenon
Carryover effect – a sequencing effect that occurs when performance in one
treatment conditions is influenced by participation in a prior treatment
condition(s)
Case – a bounded system
Case study research – research that provides a detailed account and
analysis of one or more cases
Categorical variable – a variable that varies in type or kind
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Causal modeling – a form of explanatory research where the researcher
hypothesizes a causal model and then empirically tests the model. Also
called structural equation modeling or theoretical modeling.
Causal-comparative research – a form of non-experimental research where
the primary independent variable of interest is categorical
Cause and effect relationship – when one variable affects another variable
Cell – a combination of two or more independent variables in a factorial
design
Census – a study of the whole population rather than a sample
Changing-criterion design – a single-case experimental design in which a
participant’s behavior is gradually altered by changing the criterion for
success during successive treatment periods
Checklist – a list of response categories that respondents check if
appropriate
Chi square test for contingency tables – statistical test used to determine if
a relationship observed in a contingency table is statistically significant
CIJE – an annotated index of articles from educational journals
Closed-ended question – a question that forces participants to choose a
response
Cluster -- a collective type of unit that includes multiple elements
Cluster sampling – type of sampling where clusters are randomly selected
Co-occurring codes – sets of codes that partially or completely overlap
Coding – marking segments of data with symbols, descriptive words, or
category names
Coefficient alpha – a variant of the Kuder-Richardson formula that provides
an estimate of the reliability of a homogenous test
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Cohort – any group of people with a common classification or common
characteristic
Cohort study – longitudinal research focusing specifically on one or more
cohorts
Collective case study – studying multiple cases in one research study
Complete participant – researcher becomes member of group being studied
and does not tell members they are being studied
Complete observer – researcher observes as an outsider and does not tell
the people they are being observed
Comprehensive sampling – including all cases in the research study
Concurrent validity – validity evidence obtained from assessing the
relationship between test scores and criterion scores obtained at the same
time
Confidence interval – a range of numbers inferred from the sample that has
a certain probability of including the population parameter
Confidence limits – the endpoints of a confidence interval
Confidentiality – not revealing the identity of the participant to anyone
other than the researcher and the researcher’s staff
Confounding variable – an extraneous variable that systematically varies
with the independent variable and also influences the dependent variable
Constant – a single value or category of a variable
Constant comparative method – data analysis in grounded theory research
Construct validity – evidence that a theoretical construct can be inferred
from the scores on a test
Construct – an informed, scientific idea developed or “constructed” to
describe or explain behavior
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Content validity – a judgment of the degree to which the items, tasks, or
questions on a test adequately sample the domain of interest
Contextualization – the identification of when and where an event took
place
Contingency table – a table displaying information in cells formed by the
intersection of two or more categorical variables
Control group – the group that does not receive the experimental treatment
condition
Convenience sampling – people who are available or volunteer or can be
easily recruited are included in the sample
Convergent evidence – evidence that the scores on prior tests and the
current test designed to measure the same construct are correlated
Correlation coefficient – an index indicating the strength and direction of
relationship between two variables
Correlational research – a form of non-experimental research where the
primary independent or predictor variable of interest is quantitative
Corroboration – comparing documents to each other to determine whether
they provide the same information or reach the same conclusion
Counterbalancing – administering the experimental treatment conditions to
all comparison groups, but in a different order
Criterion of falsifiability – statements and theories should be “refutable”
Criterion-related validity – a judgment of the extent to which scores from
a test can be used to predict or infer performance in some activity
Critical case sampling – selecting what are believed to be particularly
important cases
Cronbach’s alpha – see coefficient alpha
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Cross-sectional research – data are collected at a single point in time
Culture – a system of shared beliefs, values, practices, perspectives, folk
knowledge, language, norms, rituals, and material objects and artifacts that
the members of a group use in understanding their world and in relating to
others
Data set – a set of data
Data triangulation – the use of multiple data sources
Debriefing – a post study interview in which all aspects of the study are
revealed, any reasons for deception are explained, and any questions the
participant has about the study are answered
Deception – misleading or withholding information from the research
participant
Deductive reasoning – drawing a specific conclusion from a set of premises
Deductive method – a top down or confirmatory approach to science
Dehoaxing – informing participants about any deception used and the
reasons for its use
Deontological approach – an ethnical approach that says ethical issues
must be judged on the basis of some universal code
Dependent variable – a variable that is presumed to be influenced by one or
more independent variables
Description – attempting to describe the characteristics of a phenomenon
Descriptive validity – the factual accuracy of an account as reported by the
researcher
Descriptive research – research focused on providing an accurate description
or picture of the status or characteristics of a situation or phenomenon
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Descriptive statistics – division of statistics focused on describing,
summarizing, or making sense of a particular set of data
Desensitizing – reducing or eliminating any stress or other undesirable
feelings the participant may have as a result of participating in the study.
Determinism – the assumption that all events have causes
Diagnostic tests – tests designed to identify where a student is having
difficulty with an academic skill
Diagramming – making a sketch, drawing, or outline to show how
something works or to clarify the relationship between the parts of a whole
Differential attrition – when participants do not drop out randomly
Differential influence – when the influence of an extraneous variable is
different for the various comparison groups
Direct effect – the effect of the variable at the origin of an arrow on the
variable at the receiving end of the arrow
Directional alternative hypothesis – an alternative hypothesis that contains
either a “greater than” sign or a “less than” sign
Discriminant evidence – evidence that the scores on the newly developed
test are not correlated with the scores on tests designed to measure
theoretically different constructs
Disproportional stratified sampling – type of stratified sampling where the
sample proportions are made to be different from the population proportions
on the stratification variable
Double negative – a sentence construction that includes two negatives
Double-barreled question – a question that combines two or more issues or
attitude objects
Duplicate publication – publishing the same data and results in more than
one journal or in other publications
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Ecological validity – the ability to generalize the study results across
settings
Effect size indicator – a statistical measure of the strength of a relationship
Element – the basic unit that is selected from the population
Emic term – a special word or term used by the people in a group
Emic perspective – the insider’s perspective
Empirical – based on observation or experience
Empiricism – idea that knowledge comes from experience
Enumeration – the process of quantifying data
Equal probability selection method – any sampling method where each
member of the population has an equal chance of being selected
Equivalent-forms reliability – a measure of the consistency of a group of
individuals’ scores on two equivalent forms of a test measuring the same
construct
ERIC – a database containing information from CIJE and RIE
Essence – the invariant structure of the experience
Ethical skepticism – an ethical approach that says concrete and inviolate
moral codes cannot be formulated
Ethnocentrism – judging people from a different culture according to the
standards of your own culture
Ethnography – the discovery and comprehensive description of the culture
of a group of people; it’s a form of qualitative research focused on
describing the culture of a group of people
Ethnohistory – the study of the cultural past of a group of people
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Ethnology – the comparative study of cultural groups
Etic term – outsider’s words or special words that are used by social
scientists
Etic perspective – an external, social scientific view of reality
Evaluation – determining the worth, merit, or quality of an evaluation
object
Event sampling – observing only after specific events have occurred
Exhaustive categories – a set of categories that classify all of the relevant
cases in the data
Exhaustive – property that response categories or intervals include all
possible responses
Expectancy data – data illustrating the number or percentage of people that
fall into various categories on a criterion measure
Experiment – an environment in which the researcher objectively observes
phenomena that are made to occur in a strictly controlled situation in which
one or more variables are varied and the others are kept constant
Experimental group – the group that receives the experimental treatment
condition
Experimental control – eliminating any differential influence of extraneous
variables
Experimenter effect – the unintentional effect that the researcher can have
on the outcome of a study
Explanation – attempting to show how and why a phenomenon operates as
it does
Explanatory research – testing hypotheses and theories that explain how
and why a phenomenon operates as it does
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Exploration – attempting to generate ideas about phenomena
Extended fieldwork – collecting data in the field over an extended period of
time
External validity – the extent to which the study results can be generalized
to and across populations of persons, settings and times
External criticism – determining the validity, trustworthiness, or
authenticity of the source
Extraneous variable – A variable that may compete with the independent
variable in explaining the outcome; any variable other than the independent
variable that may influence the dependent variable
Extreme case sampling – identifying the “extremes” or poles of some
characteristic and then selecting cases representing these extremes for
examination
Facesheet codes – codes that apply to a complete document or case
Factor analysis – a statistical procedure that identifies the minimum number
of “factors,” or dimensions, measured by a test
Factorial design – based on a mixed model – a factorial design in which
different participants are randomly assigned to the different levels of one
independent variable but all participants take all levels of another
independent variable; it’s a design in which two or more independent
variables are simultaneously studied to determine their independent and
interactive effects on the dependent variable
Fieldnotes – notes taken by the observer
Filter question – an item that directs participants to different follow-up
questions depending on the response
Focus group – a moderator leads a discussion with a small group of people
Formative evaluation – evaluation focused on improving the evaluation
object
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Frequency distribution – arrangement where the frequencies of each
unique data value is shown
Front stage behavior – what people want or allow us to see
Fully anchored rating scale – all points are anchored on the rating scale
General linear model – a mathematical procedure that is the “parent” of
many statistical techniques
Generalize – making statements about a population based on sample data
Going native – identifying so completely with the group being studied that
you can no longer remain objective
Grounded theory – a general methodology for developing theory that is
grounded in data systematically gathered and analyzed; a qualitative
research approach
Group moderator -- the person leading the focus group discussion
Group frequency distribution – the data values are clustered or grouped
into separate intervals and the frequencies of each interval is given
Heterogeneous – a set of numbers with a great of variability
Historical research – the process of systematically examining past events
or combinations of events to arrive at an account of what happened in the
past History – any event, other than a planned treatment event that occurs
between the pre- and post measurement of the dependent variable and
influences the post measurement of the dependent variable
Holistic description – the description of how members of groups make up a
group
Homogeneity – in test validity, refers to how well a test measures a single
construct
Homogeneous sample selection – selecting a small and homogeneous case
or set of cases for intensive study
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Homogeneous – a set of numbers with little variability
Hypothesis – a prediction or educated guess
Hypothesis – a prediction or guess of the relation that exists among the
variables being investigated
Hypothesis testing – the branch of inferential statistics concerned with how
well the sample data support a null hypothesis and when the null hypothesis
can be rejected In-person interview – an interview conducted face to face
Independent variable – a variable that is presumed to cause a change in
another variable
Indirect effect – an effect occurring through an intervening variable
Inductive reasoning – reasoning from the particular to the general
Inductive codes – codes generated by a researcher by directly examining the
data
Inductive method – a bottom up or generative approach to science
Inferential statistics – division of statistics focused on going beyond the
immediate data and inferring the characteristics of population based on
samples
Inferential statistics – use of the laws of probability to make inferences and
draw statistical conclusions about populations based on sample data
Influence – attempting to apply research to change behavior
Informal conversational interview – spontaneous, loosely structured
interview
Instrumental case study – interest is in understanding something more
general than the particular case
Instrumentation – any change that occurs in the way the dependent variable
is measured
121

Intelligence – the ability to think abstractly and to learn readily from
experience
Inter-scorer reliability – the degree of agreement between two or more
scorers, judges, or raters
Interaction with selection – occurs when the different comparison groups
are affected differently by one of the threats to internal validity
Interaction effect – when the effect of one independent variable depends on
the level of another independent variable
Intercoder reliability – consistency among different coders
Interim analysis – the cyclical process of collecting and analyzing data
during a single research study
Internal consistency – the consistency with which a test measures a single
construct
Internal validity – the ability to infer that a causal relationship exists
Internal criticism – the reliability or accuracy of the information contained
in the sources collected
Internet – a network of millions of computers joined to promote
communication
Interpretive validity – accurately portraying the meaning given by the
participants to what is being studied
Interrupted time-series design – a design in which a treatment condition is
assessed by comparing the pattern of posttest responses obtained from a
single group of participants
Interval scale – a scale of measurement that has equal intervals of distances
between adjacent numbers
Intervening variable – a variable occurring between two other variables in
a causal chain
122

Interview – a data collection method where interviewer asks interviewee
questions
Interview guide approach – specific topics and/or open-ended questions
are asked in any order
Interview protocol – data collection instrument used in an interview
Interviewee – the person being asked questions
Interviewer – the person asking the questions
Intracoder reliability – consistency within a single individual
Intrinsic case study – interest is in understanding a specific case
Investigator triangulation – the use of multiple investigators in collecting
and interpreting the data
IRB – the institutional review committee that assesses the ethical
acceptability of research proposals
Item stem – the set of words forming a question or statement Kuder-Richardson
formula 20 – a statistical formula used to compute an estimate of the
reliability of a homogeneous test
Laboratory observation – observation done in a lab or other setting set up
by the researcher
Leading question – a question that suggests a researcher is expecting a
certain answer
Level of confidence – the probability that a confidence interval to be
constructed from a random sample will include the population parameter
Life-world – an individual’s inner world of immediate experience
Likert scale – a summated rating scale
Line graph – a graph that relies on the drawing of one or more lines
123

Loaded question – a question containing loaded or emotionally charged
words
Logic of significance testing – understanding and following the logical
Longitudinal research – data are collected at multiple time points and
comparisons are made across time
Low-inference descriptors – description phrased very close to the
participants’ accounts and the researchers’ field notes
Lower limit – the smallest number on a confidence interval
Main effect – the effect of one independent variable
Manipulation – an intervention studied by an experimenter
Margin of error – one half of the width of a confidence interval
Master list – a list of all the codes used in a research study
Maturation – any physical or mental change that occurs over time that
affects performance on the dependent variable
Maximum variation sampling – purposively selecting a wide range of
cases
Mean – the arithmetic average
Measure of relative standing – provides information about where a score
falls in relation to the other scores in the distribution of data
Measure of central tendency – the single numerical value that is
considered the most typical of the values of a quantitative variable
Measure of variability – a numerical index that provides information about
how spread out or how much variation is present
Measurement – the act of measuring by assigning symbols or numbers to
something according to a specific set of rules
124

Median – the 50th percentile
Median location – the numerical place where you can find the median in a
set of order numbers
Mediating variable – an intervening variable
Memoing – recording reflective notes about what you are learning from the
data
Mental Measurements Yearbook – one of the primary sources of
information about published tests
Meta-analysis – a quantitative technique used to integrate and describe the
results of a large number of studies
Method of working hypotheses – attempting to identify all rival
explanations
Method of data collection – technique for physically obtaining data to be
analyzed in a research study
Methods triangulation – the use of multiple research methods
Mixed purposeful sampling – the mixture of more than one sampling
strategy
Mode – the most frequently occurring number
Moderator variable – a variable involved in an interaction effect; see
interaction effect
Mortality – A differential loss of participants from the various comparison
groups
Multigroup research design – a research design that includes more than
one group of participants
Multimethod research – the use of more than one research method
125

Multiple operationalism – the use of several measures of a construct
Multiple regression – regression based on one dependent variable and two
or more independent variables
Multiple time-series design – an interrupted time-series design that
includes a control group to rule out a history effect
Multiple-baseline design – a single-case experimental design in which the
treatment condition is successively administered to different participants, or
to the same participant in several settings, after baseline behaviors have been
recorded for different periods of time
Multiple-treatment interference -- occurs when participation in one
treatment condition influences a person’s performance in another treatment
condition
Mutually exclusive – property that categories or intervals do not overlap
Mutually exclusive categories – a set of categories that are separate or
distinct
n – the recommended sample size
N – the population size
Naturalistic observation – observation done in “real world” settings
Naturalistic generalization – generalizing based on similarity
Negative criticism – Establishing the reliability or authenticity and accuracy
of the content of the documents and other sources used by the researcher
Negative case sampling – selecting cases that disconfirm the researcher’s
expectations and generalizations
Negative correlation – two variables move in opposite directions
Negative-case sampling – locating and examining cases that disconfirm the
researcher’s expectations
126

Negatively skewed – skewed to the left
Network diagram – a diagram showing the direct links between variables
or events over time
Nominal scale – a scale of measurement that uses symbols or numbers to
label, classify, or identify people or objects
Nondirectional alternative hypothesis – an alternative hypothesis that
includes the “not equal to” sign
Normal distribution – a unimodal, symmetric, bell-shaped distribution that
is the theoretical model of many variables
Norms – the written and unwritten rules that specify appropriate group
behavior
Null hypothesis – a statement about a population parameter
Numerical rating scale – a rating scale with anchored endpoints
Observation – unobtrusive watching of behavioral patterns
Observer-as-participant – researcher spends limited amount of time
observing group members and tells members they are being studied
Official documents – anything written or photographed by an organization
One-group pretest-posttest design – a research design in which a treatment
condition is administered to one group of participants after pretesting, but
before posttesting on the dependent variable
One-group pretest-posttest design – administering a posttest to a single
group of participants after they have been given an experimental treatment
condition
One-group posttest-only design – administering a posttest to a single group
of participants after they have been given an experimental treatment
condition
127

One-stage cluster sampling – a set of clusters is randomly selected and all
of the elements in the selected clusters are included in the sample
One-way analysis of variance – statistical test used to compare two or more
group means
Open coding – the first stage in grounded theory data analysis
Open-ended question – a question that allows participants to respond in
their own words
Operationalism – representing constructs by a specific set of steps or
operations
Opportunistic sampling – selecting cases where the opportunity occurs
Oral histories – based on interviews with a person who has had directed or
indirect experience with or knowledge of the chosen topic
Order effect – a sequencing effect that occurs from the order in which the
treatment conditions are administered
Ordinal scale – a rank-order scale of measurement
Outlier – a number that is very atypical of the other numbers in a
distribution
Panel study – study where the same individuals are studied at successive
points over time
Parameter – a numerical characteristic of a population
Partial correlation – used to examine the relationship between two
quantitative variables controlling for one or more quantitative extraneous
variables
Partial publication – publishing several articles from the data collected in
one large study; is generally not unethical for large studies
128

Participant feedback – discussion of the researcher’s conclusions with the
actual participants
Participant-as-observer – researcher spends extended time with the group
as an insider and tells members they are being studied
Path coefficient – a quantitative index providing information about a direct
effect
Pattern matching – predicting a pattern of results and determining if the
actual results fit the predicted pattern
Peer review – discussing one’s interpretations and conclusions with one’s
peers or colleagues
Percentile ranks – scores that divide a distribution into 100 equal parts
Percentile rank – the percentage of scores in a reference group that fall
below a particular raw score
Periodicity – the presence of a cyclical pattern in the sampling frame
Personal documents – anything written or photographed for private
purposes
Personality – a multifaceted construct that does not have a generally agreed
on definition
Phenomenology – the description of one or more individuals’ consciousness
and experience of a phenomenon
Pilot test – a preliminary test of your questionnaire
Point estimate – the estimated value of a population parameter
Point estimation – the use of the value of a sample statistic as the estimate
of the value of a population parameter
Population – the complete set of cases; it’s the large group to which a
researcher wants to generalize the sample results
129

Population validity – the ability to generalize the study results to the
individuals not included in the study
Positive correlation – two variables move in the same direction
Positive criticism – ensuring that the statements made or the meaning
conveyed in the various sources is correct
Positively skewed – skewed to the right
Post hoc fallacy – making the argument that because A preceded B, A must
have caused B
Post hoc test – a follow-up test to the analysis of variance
Posttest-only control-group design – administering a posttest to two
randomly assigned groups of participants after one group has been
administered the experimental treatment condition
Practical significance – a conclusion made when a relationship is strong
enough to be of practical importance
Prediction – attempting to predict or forecast a phenomenon
Predictive research – research focused on predicting the future status of
one or more dependent variables based on one or more independent
variables
Predictive validity – validity evidence obtained from assessing the
relationship between test scores collected at one point in time and criterion
scores obtained at a later time
Presence or absence technique – manipulating the independent variable by
presenting one group the treatment condition and withholding it from the
other group
Presentism – the assumption that the present-day connotations of terms also
existed in the past
130

Pretest-posttest control-group design – a research design that administers
a posttest to two randomly assigned groups of participants after both have
been pretested and one of the groups has been administered the experimental
treatment condition
Primary source – a source in which the creator was a direct witness or in
some other way directly involved or related to the event
Primary data – original data collected as part of a research study
Probabilistic cause – changes in variable A “tend” to produce changes in
variable B; it’s a cause that usually produces an outcome
Probability value – the probability of the result of your research study, or
an even more extreme result, assuming that the null hypothesis is true
Probability proportional to size – a type of two-stage cluster sampling
where each cluster’s chance of being selected in stage one depends on its
population size
Probe – prompt to obtain response clarity or additional information
Problem of induction – things that happened in the past may not happen in
the future
Problem – an interrogative sentence that asks about the relation that exists
between two or more variables
Proportional stratified sampling – type of stratified sampling where the
sample proportions are made to be the same as the population proportions on
the stratification variables
Prospective study – another term applied to a panel study
Purposive sampling – the researcher specifies the characteristics of the
population of interest and locates individuals with those characteristics
Qualitative observation – observing all potentially relevant phenomena
131

Qualitative research – research relying primarily on the collection of
qualitative data
Quantitative interview – an interview providing qualitative data
Quantitative observation – standardized observation
Quantitative variable – a variable that varies in degree or amount
Quantitative research – research relying primarily on the collection of
quantitative data
Quasi-experimental research design – an experimental research design
that does not provide for full control of potential confounding variables
primarily by not randomly assigning participants to comparison groups
Questionnaire – a self-report data collection instrument filled out by
research participant
Quota sampling – the researcher determines the appropriate sample sizes or
quotas for the groups identified as important and takes convenience samples
from these groups
Random assignment – randomly assigning a set of people to different
groups; it’s a statistical control procedure that maximizes the probability that
the comparison groups will be equated on all extraneous variables
Range – the difference between the highest and lowest numbers
Ranking – the ordering of responses into ranks
Rating scale – a continuum of response choices
Ratio scale – a scale of measurement that has a true zero point as well as all
the characteristics of the nominal, ordinal, and interval scales
Rationalism – idea that reason is the primary source of knowledge
132

Reactivity – an alteration in performance that occurs as a result of being
aware of participating in a study; it refers to changes occurring in people
because they know they are being observed
Reference group – the norm group used to determine the percentile ranks
Reflexivity – self-reflection by the researcher on his or her biases and
predispositions
Regression analysis – a set of statistical procedures used to predict the
values of a dependent variable based on the values of one or more
independent variables
Regression coefficient – the predicted change in Y given a one-unit changes
in X
Regression line – the line that best fits a pattern of observations
Regression equation – the equation that defines the regression line
Reliability – consistency or stability
Repeated sampling – drawing many or all-possible samples from a
population
Repeated-measures design – a design in which all participants participate
in all experimental treatment conditions
Replication logic – the idea that the more times a research finding is shown
to be true with different sets of people, the more confidence we can place in
the finding and in generalizing beyond the original participants
Replication – research examining the same variables with different people
Representative sample – a sample that resembles the population
Research design – the outline, plan, or strategy used to answer a research
question
133

Research ethics – a set of principles to guide and assist researchers in
deciding which goals are most important and in reconciling conflicting
values
Research hypothesis – the hypothesis of interest to the researcher and the
one he or she would like to see supported by the study results
Research method – overall research design and strategy
Research plan – the outline or plan that will be used in conducting the
research study
Research problem – see problem
Researcher bias – obtaining results consistent with what the researcher
wants to find
Researcher-as-detective – metaphor applied to researcher when searching
for cause and effect
Response rate – the percentage of people in a sample that participate in a
research study
Response set – tendency to respond in a specific direction regardless of
content
Retrospective research – the researcher starts with the dependent variable
and moves backward in time
Retrospective questions – questions asking people to recall something from
an earlier time
RIE – an index of abstracts of research reports
Rule of parsimony – selecting the most simple theory that works
Sample – the set of elements taken from a larger population
Sampling error – the difference between the value of a sample statistic and
a population parameter
134

Sampling frame – a list of all the elements in a population
Sampling with replacement – it is possible for elements to be selected
more than once
Sampling without replacement – it is not possible for elements to be
selected more than once
Sampling interval – the population size divided by the desired sample size;
it is symbolized by “k”
Sampling distribution – the theoretical probability distribution of the
values of a statistic that results when all possible random samples of a
particular size are drawn from a population
Sampling error – the difference between a sample statistic and the
corresponding population parameter
Sampling distribution of the mean – the theoretical probability distribution
of the means of all possible random samples of a particular size drawn from
a population
Scatterplot – a graph used to depict the relationship between two
quantitative variables
Science – an approach for the generation of knowledge
Secondary data – data originally collected at an earlier time by a different
person for a different purpose
Secondary source – a source that was created from primary sources,
secondary sources, or some combination of the two
Segmenting – dividing data into meaningful analytical units
Selection – selecting participants for the various treatment groups that have
different characteristics
Selection by history interaction – occurs when the different comparison
groups experience a different history event
135

Selection by maturation interaction – occurs when the different
comparison groups experience a different rate of change on a maturation
variable
Selection-maturation effect – when participants in one of two comparison
groups grow or develop faster than participants in the other comparison
group
Selective coding – the final stage in grounded theory data analysis
Semantic differential – a scaling technique where participants rate a series
of objects or concepts
Sequencing effects – biasing effects that can occur when each participant
must participate in each experimental treatment condition
Shared values – the culturally defined standards about what is good or bad
or desirable or undesirable
Shared beliefs – the specific cultural conventions or statements that people
who share a culture hold to be true or false
Significance level – the cutoff the researcher uses to decide when to reject
the null hypothesis
Significance testing – a commonly used synonym for hypothesis testing
Simple random sample – a sample drawn by a procedure where every
member of the population has an equal chance of being selected
Simple case – when there is only one independent variable and one
dependent variable
Simple random sampling – the term usually used for sampling without
replacement
Simple case of correlational research – when there is one quantitative
independent variable and one quantitative dependent variable
136

Simple regression – regression based on one dependent variable and one
independent variable
Simple case of causal-comparative research – when there is one
categorical independent variable and one quantitative dependent variable
Single-case experimental designs – designs that use a single participant to
investigate the effect of an experimental treatment condition
Skewed – not symmetrical
Snowball sampling – each research participant is asked to identify other
potential research participants
Social desirability response set – tendency to provide answers that are
socially desirable
Sourcing – information that identifies the source or attribution of the
document
Spearman-Brown formula – a statistical formula used for correcting the
split-half reliability coefficient for the shortened test length created by
splitting the full-length test into two equivalent halves
Split-half reliability – a measure of the consistency of the scores obtained
from two equivalent halves of the same test
Spurious relationship – when the relationship between two variables is due
to one or more third variables
Standard error – the standard deviation of a sampling distribution
Standard deviation – the square root of the variance
Standard scores – scores that have been converted from one scale to
another to have a particular mean and standard deviation
Standardization – presenting the same stimulus to all participants
137

Standardized open-ended interview – a set of open-ended questions are
asked in a specific order and exactly as worded
Starting point – a randomly selected number between one and k
States – distinguishable, but less enduring ways in which people differ
Static-group comparison design – comparing posttest performance of a
group of participants who have been given an experimental treatment
condition with a group that has not been given the experimental treatment
condition
Statistic – a numerical characteristic of a sample
Statistical regression – the tendency of very high scores to become lower
and very low scores to become higher on post testing
Statistically significant – a research finding is probably not attributable to
chance; it’s the claim made when the evidence suggests an observed result
was probably not due to chance
Stratification variable – the variable on which the population is divided
Stratified sampling – dividing the population into mutually exclusive
groups and then selecting a random sample from each group
Structural equation modeling – see causal modeling
Summated rating scale – a multi-item scale that has the responses for each
person summed into a single score
Summative evaluation – evaluation focused on determining overall
effectiveness of the evaluation object
Survey research – a term sometimes applied to non-experimental research
based on questionnaires or interviews
Synthesis – the selection, organization and analysis of the materials
collected
138

Systematic sample – a sample obtained by determining the sampling
interval, selecting a random starting point between 1 and k, and then
selecting every kith element
t test for correlation coefficients – statistical test used to determine if a
correlation coefficient is statistically significant
t test for independent samples – statistical test used to determine if the
difference between the means of two groups is statistically significant
t test for regression coefficients – statistical test used to determine if a
regression coefficient is statistically significant
Table of random numbers – a list of numbers that fall in a random order
Target population – the larger population to whom the study results are to
be generalized
Telephone interview – an interview conducted over the phone
Temporal validity – The extent to which the study results can be
generalized across time
Test-retest reliability – a measure of the consistency of scores over time
Testing – any change in scores obtained on the second administration of a
test as a result of having previously taken the test
Tests in Print – A primary source of information about published tests
Theoretical sensitivity – when a researcher is effective at thinking about
what kinds of data need to be collected and what aspects of already collected
data are the most important for the grounded theory
Theoretical validity – the degree to which a theoretical explanation fits the
data
Theoretical saturation – occurs when no new information or concepts are
emerging from the data and the grounded theory has been validated
139

Theory – an explanation or an explanatory system; a generalization or set of
generalizations used systematically to explain some phenomenon
Theory triangulation – the use of multiple theories and perspectives to help
interpret and explain the data
Think-aloud technique – has participants verbalize their thoughts and
perceptions while engaged in an activity
Third variable – a confounding extraneous variable
Third variable problem – an observed relationship between two variables
may be due to an extraneous variable
Three necessary conditions – three things that must be present if you are to
contend that causation has occurred
Time interval sampling – checking for events during specific time intervals
Transcription – transforming qualitative data into typed text
Trend study – independent samples are taken from a population over time
and the same questions are asked
Two-stage cluster sampling – first a set of clusters is randomly selected
and second a random sample of elements is drawn from each of the clusters
selected in stage one
Type I error – rejecting a true null hypothesis
Type II error – failing to reject a false null hypothesis
Type technique – manipulating the independent variable by varying the
type of variable presented to the different comparison groups
Typical case sampling – selecting what are believed to be average cases
Typology – a classification system that breaks something down into
different types or kinds
140

Unrestricted sampling – the technical term used for sampling with
replacement
Upper limit – the largest number on a confidence interval
Utilitarianism – an ethical approach that says judgments of the ethics of a
study depend on the consequences the study has for the research participants
and the benefits that may arise from the study
Vagueness – uncertainty in the meaning of words or phrases
Validation – the process of gathering evidence that supports and inference
based on a test score or scores
Validity coefficient – a correlation coefficient computed between test scores
and criterion scores
Validity – a judgment of the appropriateness of the interpretations,
inferences, and actions made on the basis of a test score or scores
Variable – a condition or characteristic that can take on different values or
categories
Variance – a measure of the average deviation from the mean in squared
units
Y-intercept – the point where the regression line crosses the Y-axis
z-score – a raw score that has been transformed into standard deviation units
141

PART III:
Partial Listing of
Selected References
and Acknowledgements
142

Partial Listing of Selected References and Acknowledgements
PROCEDURES IN EDUCATIONAL RESEARCH AND
BASIC STATISTICS
Preliminary First Edition/Copyrights Pending/NOT FOR SALE
2005
Directories
American Educators Encyclopedia, 1991
A Critical Dictionary of Educational Concepts, 2nd edition, 1990
The Educator’s Desk Reference: A Sourcebook of Educational Information
and Research, 1989
Patterson’s American Education, 2000
Dictionaries and Encyclopedias
A Critical Dictionary of Educational Concepts: An Appraisal of Selected
Ideas and Issues in Educational theory and Practice, 1990
Encyclopedia of Educational Research 6th edition, 1992
Encyclopedia of Ethics, 1992
Encyclopedia of Learning and Memory, 1992
The Facts on File Dictionary of Education, 1988
The International Encyclopedia of Education Research and Studies, 1994
World Education Encyclopedia, 1988
The World of Learning, 2000
Yearbooks and Handbooks
Educator’s Handbooks: A Research Perspective, 1987
International Handbook of Education Systems, Vol. 1: Europe and Canada.
143

Vol. North Africa and the Middle East. Vol. 3: Asia, Australasian and Latin
America, 1988
Statistical Yearbook/Annuaire/Statistique/Annuario Estadistico, 1984
Comprehensive Dissertation Index 1861-1972; 1973+
Dissertation Abstracts Online 1861 – Accessible only from Mugar Reference
Department – Abstracts from 1980+
The Dissertation Handbook: A Guide to Successful Dissertations, 2nd
edition, 1993
Statistics
Black Americans: A statistical Sourcebook, Education Reference X E 185.5
B63 1990 – Mugar Reference X E 185.5 B63 2000
The Condition of Teaching: A State-by-State Analysis – Mugar Reference X
LB 2832.2 C66, 1988
Digest of Education Statistics – Mugar Reference X L 112 F62
Education at a Glance: OECD Indicators – Mugar Reference X LB 2846
B56, 2000
Index to International Statistics – Mugar Reference X Z 7552 153
The National Education Goals Report: Building a Nation of Learners,
Education Reference X LA 210 N37
Public Schools USA: A comparative Guide to School Districts, Education
Reference X LA 217.2 H37, 1991
Status of the American Public School Teacher, Education Reference X LB
283.2 S7 1987 – Mugar Reference X LB 283.2 1987
UNESCO Statistical Digest, education Reference X L 11 S863
World Education Report, 1991
144

Periodicals
American Educational Research Journal
American Journal of Education
Basic Education
Comparative Education Review
The Education Digest
The Educational Forum
Educational Research
Educational Studies
Educational Theory
Estimates of School Statistics
Harvard Educational Review
International Journal of Scholarly Academic Intellectual Diversity
International Forum of Educational Renewal
International Review of Education
Journal of Education
Journal of Educational and Behavioral Statistics
The Journal of Educational Research
National FORUM of Educational Administration and Supervision Journal
http://www.nationalforum.com/
National FORUM of Applied Educational Research Journal
http://www.nationalforum.com/
National FORUM of Teacher Education Journal
http://www.nationalforum.com/
National FORUM of Special Education Journal
http://www.nationalforum.com/
145

On-Line Scholarly Electronic Journal Division of National FORUM
Journals
http://www.nationalforum.com/
Peabody Journal of Education
Rankings of the States
Research in Education
Review of Educational Research
Review of Research in Education
Teaching and Teacher Education
Review of Research in Education
Teaching and Teacher Education
The Yearbook of the National Society for the Study of Education
Web Sites
American Demographics www.umich.edu/-nes
Bureau of Economic Analysis www.bea.doc.gov
Bureau of Labor Statistics www.stats.bis.gov
Condition of Education
Nces.ed.gov. /pubsearch/pubsinfo.asp? pubid=1999022
Digest of Education Statistics nces.ed.gov/pubs2000/digest99
Encyclopedia of Education statistics nces.ed.gov/edstats
Eurostate europa.eu.int/comm../eurostat
Ferret www.edc.gov/nchs/datawh/ferret/htm
Fed Stats www.f3dstats.gov
International Archives of Education Data www.icpso.umich.edu/IAED
Inter-university Consortium of Political and Social Research
146

www.lib.lsu.edu/gov/fedgov.html
National Center for Education Statistics nces.ed.gov
National Center for Education Statistics – Search Tools and Related
Information nces.ed.gov/pubsearch
National Center for Education Statistics – Survey and Program Areas
nces.ed.gov/surveys
National Center for Health Statistics www.cdc.gov/nchs/default.htm
NATIONAL FORUM JOURNALS www.nationalforum.com
Projections of Education Statistics to 2009
nces.ed.gov/pubsearch/pubsinfo.asp?pubid=1999038
The Qualitative Report www.nove.edu/ssss/OR
Research and Statistics www.ed.gov/stats.html
Research Reports from The National Research and Development Centers
http://research.cse.ucla.edu
STAT-USA Internet www.stat-usa.gov
Statistical Abstracts of the United States www.census.gov/state_abstract
Statistical Resources on the Web
www.lib.umich.edu/libhome/Documents.Center/Stats.html
University of Michigan Documents Center: Statistics Section
www.lib.umich.edu/libhome/Documents.center
University of Virginia Social Science Data Center
www.lib.virginia.edu/social/interactives.html
147

Testing and Assessment
Boston.com-MCAS Tests
Educational Testing Service Index ericae.net/testcol.htm#ETSTF
Test Locator www.ericae.net/testcol.htm
UNESCO
Research Centers and Education Laboratories
American Education Research Association www.aera.net
Center for Applied Linguistics www.cal.org/crede
Center for Research on Education, Diversity, and Excellence (CREDE)
Center for Research on Evaluation, Standards, and Student Teaching
(CRESST) cress96.cse.ucla.edu
Center for Research on the Education of Students Placed At-Risk
(CRESPAR) www.csos.jhu.edu/crespar/CreSPaR.html
Center for the Improvement of Early Reading Achievement (CIERA)
www.ciera.org
Center for the Study of Teaching and Policy (CTP)
Depts., Washington.edu/ctpmail
Common Core of Data: Information on Public Schools and School Districts
in the United States nces.ed.gov/ccd/ccddata.html
National Center for Early Development and Learning (NCEDL)
www.fpg.unc.edu/~ncedl
National Center for Improving Student Learning and Achievement in
Mathematics and Science (NCISLA) www.wcer.wise.edu/NCISLA
National Center for Postsecondary Improvement (NCPI) ncip.Stanford.edu
National Center for the Study of Adult Learning and Literacy (NCSALL)
148

Gseweb.Harvard.edu/-ncsall
National Center on the Gifted and Talented (NRC/GT)
www.gifted.uconn.edu/nrcgt.html
National Center on Increasing the Effectiveness of State and Local
Education Reform Efforts www.upenn.ed/gse/cpre
National Research and Development Center on English Learning &
Achievement (CELA) eela.Albany.edu
Research Reports from The National Research and Development Centers,
research.cse.ucla.edu Ask ERIC www.askeric.org
ED Pubs www.ed.gov/pubs/edpubs.html
Educational Research and Improvements Reports and Studies
www.ed.gov/pubs/studies.html
Education Resource Organizations Directory www.ed.gov/Programs/EROD
Educational Resources Information Center (ERIC) www.accesseric.org
ERIC Clearinghouses www.accesseric.org/sites/barak.html
ERIC Digests www.ed.gov/databases/ERIC_Digests/index
ERIC Document Reproduction Service www.edrs.com
ERIC/AE full Text Internet Library www.ericae.net/ftlib.htm
How to get copies of ERIC Database Materials
www.accesseric.org/resources/pocket/materials.html
Massachusetts Department of Education www.doe.mass.edu
National Library of Education www.ed.gov/NLE
Office of Educational Research and Improvement (OERI)
www.bu.ed/library/research-guides/eduresearch.html
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Search the ERIC Database accesseric.org/searchdb/searchdb.html
State Departments of Education www.ed.gov
Other Selected References
Aiken, L. R. (1988). Psychological Testing and Assessment. Boston, MA:
Allyn & Bacon.
Babbie, E.R. (1989). The Practice of Social Research (5th Edition).
Belmont, CA: Wadsworth
Best, J. & Kahn J. (1998). Research in Education (8th Edition). Boston, MA:
Allyn & Bacon
Borich, G. & Kubiszyn, T. (2000). Educational Testing and Measurement
(6th Edition). New York, NY: John Wiley & Sons.
Charles, C. M. & Mertler, C. A. (2002). Introduction to Educational
Research (4th Edition). Boston, MA: Allyn & Bacon.
DeMoulin, D.F., Kritsonis, W.A. (2009) A Statistical Journal: Taming of the
Skew. Murrieta, CA: AlexisAustin
Dillamn, D. (1978). Mail and Telephone Surveys: The Total Design Method.
New York, NY: John Wiley & Sons.
Gall, J. P., Gall, M. D., and Borg, W. R. (2005). Applying Educational
Research: A Practical Guide. Boston, MA: Pearson.
Johnson, B., and Christensen, L. (2004). Educational Research: Quantitative,
Qualitative and Mixed Approaches. Pearson Education Inc., Boston, MA:
Allyn and Bacon
Kritsonis, W. A. (2002). William Kritsonis, PhD on SCHOOLING.
Mansfield, OH: BookMasters.
Kritsonis, W. A. (2003). Procedures in Educational Research and Design.
Mansfield, OH: BookMasters.
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Mertler, C. (2003). Classroom Assessment. Los Angeles, CA: Pyrczak
Publishing
Popham, W. & Sirotnik, K. (1973). Educational Statistics (2nd Edition).
New York, NY: Harper & Row
Spatz, C. & Johnson, J. (1989). Basic Statistics. Pacific Grove, CA: Brooks/
Cole Publishing.
Spcinthall, R. (2000). Basic Statistical Analysis (6th Edition). Boston, MA:
Allyn & Bacon.
Spcinthall, R., Schmutte, G. T., & Sirois, L. (1990). Understanding
Educational Research. Englewood cliffs, NJ: Prentice-Hall.
Stigler, S. (1986). The History of Statistics. Cambridge, MA: Harvard
University Press.
Worthen, B. & Sanders, J. (1987). Educational Evaluations. New York,
NY: Longman
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PART IV:
About the Authors
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ABOUT THE AUTHORS
William Allan Kritsonis, PhD is Editor-in-Chief of the NATIONAL FORUM
JOURNALS. He is a tenured professor in the PhD Program in Educational
Leadership at Prairie View A&M University/Member Texas A&M University
System. He was a Visiting Lecturer (2005) at the Oxford Round Table, Oriel
College in the University of Oxford, Oxford, ENGLAND. Dr. Kritsonis is also a
Distinguished Alumnus (2004) at Central Washington University in the College of
Education and Professional Studies, Ellensburg, Washington. He has authored or
co-authored numerous articles and conducted several research presentations with
students and colleagues in the field of education. Dr. Kritsonis has served education
as a school principal, superintendent of schools, director of field experiences and
student teaching, consultant, and professor.
Kimberly Grantham Griffith, Ph.D., is Editor of THE LAMAR UNIVERSITY
ELECTRONIC JOURNAL OF STUDENT RESEARCH. She is a tenured associate
professor in the Department of Professional Pedagogy at Lamar University/Member
Texas State University System. Dr. Griffith is also a Councilor (board member) for
the At-Large Division, Council for Undergraduate Research (CUR). In April 2000,
she received the prestigious Lamar University Merit Award for teaching excellence.
Dr. Griffith serves on the editorial board of the Electronic Journal of Inclusive
Education. She has co-authored numerous articles and conducted several research
presentations with students and colleagues in the field of education.
Cristian Bahrim, Ph.D., is an assistant professor in the Department of Chemistry
and Physics at Lamar University and holds a joint-appointment in the Department
of Electrical Engineering. He is (co-)author in several papers published in peer-
reviewed journals/books and conferences’ proceedings. He conducted several
research projects in the field of atomic physics, optics, lasers, astronomy and physics
education. Since 2001, Dr. Bahrim has served as reviewer for the Journal of Physics of
the Institute of Physics (England), and recently he joined the editorial board of “The
Lamar University Electronic Research Journal of Student Research”. Dr. Bahrim
received the M.S. degree in Physics from University of Bucharest in 1991 and the Ph.D.
degree in Physics from University of Paris in 1997. He held a research associate position
in Kansas State University (1999-2001) and he was research assistant in the Institute of
Atomic Physics, Romania (1991-1998). He obtained two outstanding McNair Mentor
awards in 2005. Since 2000, he was selected in several Marquis® Who’s Who
publications. Dr. Bahrim was the recipient of a French Government Scholarship
(1991-1996).
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David E. Herrington, Ph.D., is an assistant professor in the Department of
Educational Leadership and Counseling at Prairie View A&M University/Member
Texas A&M University System. He has supervised more than 2,000 student
research field-based projects. Dr. Herrington has co-authored numerous articles
with students in the area of education. He believes that everyone uses statistical
thinking and inductive reasoning in everyday life. Making students aware of their
existing skills and knowledge in these areas provides them with a sense that, in
many ways, statistical reasoning and scientific processes are familiar. The value of a
statistics course comes from the development of the specialized vocabulary,
participative data gathering methods, and data analysis techniques that can enhance
or leverage existing conceptual frameworks that students bring into the learning
process.
Robert L. Marshall, EdD is the Senior National Editor of NATIONAL FORUM
JOURNALS. He is a tenured professor at Western Illinois faculty in the
Educational Leadership Department. His background in education spans over 25
years and includes teaching in secondary public schools, campus as well as district
level administrative experience and ten year in higher education as a professor of
Educational Leadership in the Texas A&M University System. Dr. Marshall's
research interests are in the areas of distance education, secondary student success
initiatives along with studies related to the principalship and superintendency in
public schools.
Copyright © 2006 William Allan Kritsonis, Ph.D.
ALL RIGHTS RESERVED/FOREVER
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