Download - RESEARCH (Practical Applications) by William Allan Kritsonis, PhD

Research(Practical Applications)

William Allan Kritsonis, PhD

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Copyright © 2011 by William Allan Kritsonis

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By

William Allan Kritsonis, PhDProfessor

PhD Program in Educational LeadershipPrairie View A&M University

Member of the Texas A&M University SystemPrairie View, Texas 77446

Distinguished Alumnus (2004)Central Washington University

College of Education and Professional StudiesEllensburg, Washington

Invited Guest Lecturer (2005)Oxford Round TableUniversity of Oxford

Oxford, England

Doctor of Humane Letters (2008)School of Graduate Studies

Southern Christian University

Hall of Honor (2008)William H. Parker Leadership Academy

Prairie View A&M UniversityThe Texas A&M University System

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Dedication

This book is dedicated to any person that has taken a class from me over the years. William Allan Kritsonis, PhD

ACKNOWLEDGEMENTS

The purpose of this attempt 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. Any omissions are not intentional.

CONTENTS

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Page

PART I: Practical Applications of 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

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Chapter 13: Descriptive Statistics .....................................................................81

Chapter 14: Types of Distributions ...................................................................88

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 for 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 Author ........................................................................154

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PART I:

Practical Applications of Research and Basic Statistics

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Chapter 1 – William Allan Kritsonis, PhD

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 thinkersb. Authority versus empirical evidencec. Elders versus personal experience

6. People eventually began to think systematically. A few great thinkers led the way.

7. Aristotle (Ancient Greece)

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a. First approach to reasoning.b. Deductive Method - moving from general assumptions to specific

Syllogism1) 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 phenomenab. 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 problemb. Formulate a hypothesisc. Collect, organize, and analyze datad. Formulate conclusionse. 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.

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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.

12. Science

1) Definition: An approach to the gathering of knowledge, rather than a field of study.

2) Two Functions of Sciencei. Develop theory

ii. Test hypotheses deduced from theory

13. The Way a Scientist Works

a. Empirical Approach - collect datab. 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 developmentb. Behavior of gases - Air-conditioning, refrigerationc. Atomic Theory - Nuclear powerd. 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 outcome2) 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.

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b. Null Hypothesis (Symbol=Ho)

1) This hypothesis is stated negatively so that the logic of statistical analysis can be applied.

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-----------------------parameterb. Sample---------------------------statisticc. Sample: a small proportion of a population selected for observation

and analysisd. 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 numberc. 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.

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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

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, bute. 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.

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20. Two ways to Classify Researcha. 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.

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 Researcha. 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 is1) Basically, data are interpreted without numerical analysis.2) Interviews, videos, and other methods are used to gather

information.

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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

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Chapter 2 - William Allan Kritsonis, PhD

Historical ResearchKey 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

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5. Characteristics of Historical Research

a. Guided by hypotheses or questions to be answeredb. Systematic collection of datac. Objective evaluation of datad. Limited to available datae. Explanation—not just rehashing of the past—explains why it

happened as it didf. May investigate individuals, ideas, movements, institutions, cultural

circumstances, and movementsg. 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 research2) No objectivity3) Often second—not firsthand information4) 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 variablesf. Uniqueness cannot be replicated.

7. Steps in Historical Research

a. Define the problemb. Formulate the hypothesis or questions to be answeredc. Collect data

1) Primary sources2) Secondary sources

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d. Analyze the data1) External criticism—authenticity

i. Was this person really present?ii. Is this a real document from that time period?

2) Internal criticism—accuracyi. Did the person give an unbiased account of what happened?

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 data1) Conclusions2) Generalizations3) Explanation or hypothesis

f. Report findings and conclusions

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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.

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Descriptive ResearchKeyPoints

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.

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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 correlationCoefficient 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.

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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 DESCRIPTIVE RESEARCH

SIMILARITIES WITH OTHER TYPES OF DESCRIPTIVE RESEARCH

DIFFERENCES WITH OTHER TYPES OF DESCRIPTIVE RESEARCH

EXAMPLES

1. Surveys Very similar to polls in that you collect data according to a set of questions.

Use of a large number of cases to describe to a general population. You can collect data on attitudes as well as other practices, occurrences, etc. Polls are usually much smaller and are the collection of attitudes.

a. restaurant questionnaireb. general satisfaction survey for products purchasedc. (add more to the list)

2.

3.

4.

5.

6.

7.

8.

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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 individual’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.

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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 Groupinga. Experimental Group vs. Control Group

1) Experimental Group : group exposed to variable under consideration

2) Treatment Group : same as experimental group3) 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 researcher2) Organismic Variable : attribute of the subjects that cannot be

controlled.

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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 selectiong. 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.

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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.

8. Controlling Threats to Experimental Validity

a. Remove the Variable: variable is not considered in results.

b. Matching cases: selecting pairs with identical characteristics and assigning them to different groups

The experimental model comes from agricultural research.

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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 subjects2) Control of variables: independent and confounding3) The gathering and treatment of data4) Development of hypothesis5) 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

11. In studying experimental design, the following Campbell and Stanley symbols are used:

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a. R random assignment of subjectsb. X exposure of a group to a treatmentc. C exposure of a group to a control or placebo conditiond. O observation or test administered (data gathered)

12. What makes a good study?

a. Having a control group andb. Using random selection

13. Pre-experimental Designs

a. The One-Shot Case Study Design1) X O2) No random selection and no control group

b. The One-Group, Pretest, Posttest Design1) O X O2) No random selection, no control group, and interference of

variables

c. The Static-Group Comparison Design1) X O

C O2) No random selection

14.True Experimental Design

a. The Posttest-Only, Equivalent-Groups Design

1) R X O R C O

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.

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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 O2) Has random selection; has control group3) Difficult to find enough subjects

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15.Quasi-Experimental Designs

a. The Pretest-Posttest Nonequivalent-Groups Design

1) O X O O C O2) No random selection3) Pretest is used as covariate.

b. The Time-Series Design

1) O O O O X O O O O2) No random selection

c. The Equivalent Time-Samples Design

1) O X O X O X O X O2) No random selection

d. The Equivalent Materials, pretest, Posttest Design

1) O X O O X O2) No random selection3) Can be conducted with just one group or two separate groups

16. Factorial Designs: used when more than one independent variable is involved

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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 is your hypotheses/Research Question? (Research and null)

5. What would you do to conduct the research? (Steps, who to talk with,

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?

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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.

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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.

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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?

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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.

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3. Correlation Coefficient: The procedure quantifies the relationship of paired variables.Example:

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.

.7.8.9 10-1

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6. Types of Reliability of Test or Questionnaire/Opinionnaire

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, answeronly 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.

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f. Clear directions, define important terms

g. Avoid asking two questions in one item: Keep questions short and concise.

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 letter2) Permission letter3) 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.

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j. Inform participants the questionnaire is coded.

k. Scale to use.

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.

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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.

39


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) Examplesi. Social security numbers

ii. License plate numbersiii. Bank account numbersiv. Student identification numbers

c. Not very useful in research

2) Ordinal a. Names, classifies, and ranks someone or something

b. Examplesi. 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

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2) Ratio a. Names, classifies, ranks, has equal intervals, and has a true zero b. Examples

i. Test scoresii. 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 Tendencies1) The mean is the arithmetic average.

i. The symbol for the mean is .

ii. b.

iii. The mean indicates the arithmetic midpoint; it is the best measure of centrality.

iv. Example:24 4.8 = 5 5 24.06 207 40

= 24 40

N = 5

= 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.

41

d. One purpose of the mean and median is to represent the “typical” score.

d. 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:

2) Formula:

c. Variance: the average squared units of deviation from the mean

1) Symboli. Sample:

ii. Population:

2) Formulas:

i.

ii.

iii. The variance is a value that describes the distance that scores are dispersed or spread from the mean.

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iv. This value is very useful in describing the characteristics of a distribution.

d. Standard Deviation: average units of deviation from the mean

1) Symboli. Sample:

ii. Population:

2) Formulasi.

ii.

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.

6. Normal curve

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68.26%

(Standard Deviation)

34.13% 34.13%

13.59% 13.59%

2.15% 2.15%

.12% .12%

-4 4-3 -2 -1 0 1 2 3

Percent of casesunder portions of the normal curve

95.44%

99.74%

99.98%

Percentage offrequencies in anormal distribution

Very few scores will extend above or fall below

three standard deviations from the mean.

44

7. Normal Distribution Percentiles

8. Two Ways of Computing Variance and Standard Deviation

a. Conceptual Way:

(raw score)


34.13% 34.13%

13.59% 13.59%

2.15% 2.15%

.12% .12%

-4 4-3 -2 -1 0 1 2 3.1% 2.3% 15.9% 50% 84.1% 97.7% 99.9% (Percentiles)

Percent of casesunder portions of the normal curve

Very few scores will extend above or fall below

three standard deviations from the mean.

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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=30 0 40

Measures of Central Tendencies

Measures ofVariability

b. Computational Way

(Sum of Squares)

(Variance)

(Standard deviation)

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2 4

4 16 6 36 8 64 10 100

30 220

65 30

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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 TestX Y

John 1 2

Bob 2 4

Mark 3 6

Bill 4 8

Jeff 5 10

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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 TestX 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

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10. Three ways to Interpret Coefficient of Correlation (Pearson’s r)

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

↑ ↑ Sum of Squares Sum of Squares

of X of Y

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2) Example

John 1 1 2 4 2Bob 2 4 2 4 4Bill 3 9 3 9 9Joe 4 16 4 16 16Sam 5 25 5 25 25

15 55 16 58 56

Pearson’s (very high correlation)

X and Y have a lot in common.

(Given X, one could tell 94% of the time what Y would be.

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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

raw scoremeanstandard deviation

12. Normal Distribution Problems

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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 ______






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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 ______

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 ______

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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.

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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.

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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.

95%

5%

X

Rejection Area

Acceptance Area

+1.65 (z score)

95% Acceptance Area

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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.

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

47.5%

2.5%

X

Rejection Area

Acceptance Area

+1.96

95% Acceptance Area

Acceptance Area

47.5%

Rejection Area

2.5%

-1.96

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6. Degrees of Freedom

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) Characteristics2) No population mean3) No 4) Compares the means of two different independent groups5) Example6) 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

Independent t

(Degrees of Freedom) 1

NN

SSSS

YX

YX

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4. Used in medical, agricultural, and educational research

b. Correlated t Test (paired) (very useful test)

1) Characteristicsi. Pre and post tests (pairs)

ii. Only involves one groupiii. c.

2) Formula

Correlated t

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.)

c. Analysis of Variance (ANOVA)

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1) The Independent t Test is a subset of ANOVA.

2) Characteristicsi. 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) Characteristicsi. Measures the degree of relation between two variables.

ii. Determines the degree of linear relationship between two variables.

2) Formula


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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 Pagea. 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.

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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 Problem2) Purpose of the Study3) 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?

7. Hypotheses

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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.

11. Methodology

a. This section includes the following four parts:

1) Subjects

64

i. Describe subjects or sample (who and where).ii. The population may be described in this part.

2) Instrumenti. Give details about the test or instrument and specific

materials.ii. Validity and reliability may be discussed.

3) Proceduresi. 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 Analysisi. 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.

13. References

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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.

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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.)

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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 Study2) Statement of the Problem3) Purpose of the Study4) Research Questions and/or Hypotheses5) Definitions6) Assumptions7) Limitations8) Significance of the Study

b. This chapter is basically the proposal minus the Review of the Literature and the Methodology.

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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

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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.

70

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 so forth)________ 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)________ Recommendations (practical suggestions for implementation of findings)

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________ Recommendation for further study

ORGANIZATION AND STRUCTURE OF THE DOCUMENT1. Copyright Page2. Title Page3. Signature Page4. Abstract 5. Dedication Page6. Acknowledgments 7. Table of Contents 8. List of Tables9. List of Figures10. Body text, divided into chapters designated by upper case Roman numerals 11. References in the specified style manual format12. Appendices and supporting documents 13. Human Subjects Review Approval document14. Author’s Vita

TABLES/FIGURES 1. Tables and/or figures should appear no more than one page from where they are first

referenced2. 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

SPACING1. Double spaced throughout the document 2. Indent each paragraph first line .05”

PAPER 1. 100 percent cotton, 20-pound bond

FONT AND SIZE1. Arial, Bookman, Times New Roman or similar font recommended2. Size: Standard 12 font

PAGINATION 1. Every Page should be assigned a number2. Preliminary pages, small Arabic numbers (i, ii, iii, iv …etc) in the center at bottom of

each numbered page3. Abstract receives the first numbering at the bottom and in the center4. First page of each chapter should be in the center at the bottom of the page in the

footer5. All other pages should have numbers in the upper right hand side of the page

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Dissertation Web Resources:

http://wwwnationalforum.com This site provides numerous articles dealing with a wide variety of topics.

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.

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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 numbers2) Includes the mean, median, mode (Measures of Central

Tendencies)

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b. Inferential Statistics

1) Definitionsi. A method of reaching conclusions about unmeasurable

populations using sample evidence and probability

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

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 States2) All fifth graders in Texas3) All fifth graders in Waller County

5 million 5th grade students (population) Teach using Method A

100 students randomly selected

(sample of above set)

Teach using Method B

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5. Sample

a. Definition: A subset of a population

b. Example

1) Of five million fifth grade students (population), 100 students were randomly selected (sample).

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.

60 malestudents

40 femalestudents

[Each is a sub sample of the above 1]

2)

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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

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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) Examplesi. 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 orders3) Examples

i. Class rank (1st, 2nd, 3rd, etc.)

ii. Rank by height

iii. Sports rank

iv. Rank in a contest

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4) More useful than nominal but still not that useful5) Not exact6) Hides things7) 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.

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%

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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

2. Parametric Data: Data which 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) Examplesi. 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.

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As you move farther from the average, the percentage gets smaller.

Scales of Different Types of Data

Nonparametric Data (non-normal) (discrete data – just there)1. Nominal2. Ordinal

Parametric Data (assumes normality) (continuous)3. Interval Mathematical operations can be4. Ratio computed with these types of data.

½ of the scores½ of the scores

average

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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) Examplesi.

(no score occurs more than any other)

bimodal trimodal

middle score

30

Summation of

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ii.

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.

Note: To obtain the median, find the average of the two middle numbers, 5 and 6.

5.5 = median + 6 2 11.010

10 10

To obtain the median, take the average of the two middle numbers

49

Summation of

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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

5. Characteristics of a Positively Skewed Distribution

a. The mean, median, and mode are not located at the same point.

NormalDistribution

The same amount of numbers are on either side of 60; therefore, the

mean, median, and mode are located at the same place.

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.

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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. Example57 58 58 59 59 60 60 60 61 61 62 69

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) Example50 59 59 60 60 60 61 61 62

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.

The median is 60, however 69 is the outliner and causes the mean to begreater than the median.

The median is 60, however 50 is the outliner and causes the mean to belower than the median.

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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 scorea. 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

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

for Mean

Population Sample

or or or or

(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.

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Sum of Squares:

Variance:

(for a population) (for a sample)

Standard Deviation:

Central Measures and Variability

Conceptual Way (slow way)

Raw Deviation Sum of Variance Standard Deviation Scores Mean from Mean Scores (for population) (for population)

Computational Way (fast way)

Raw Variance Standard DeviationScores (for population and sample) (for population)

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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 1411 12 13 1412 13 14 1513 20 16 1513 20 17 1813 23 18 1814

5) 6) 7) 8)

3 3 3 33 6 9 83 9 12 114 12 12 124 15 12 12

5 15 12 13

9) 10) 11)

2 4 1 4 4 1

6 4 10

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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%

-4 4-3 -2 -1 0 1 2 3.1% 2.3% 15.9%50% 84.1% 97.7% 99.9%

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2. Platykurtic Distribution

a. This distribution is basically flat.

b. It has the most variability.

c. Example:

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.)

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

55555

4 5 6732

KURTOSIS IS THE TERM USED TO DESCRIBE THEVARIABILITY (SPREAD) OF A DISTRIBUTION.

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FORMULAS

Name of Test Characteristics Formula

z

one sample based on

normal distribution

(population standard deviation) is known

critical z is always at alpha level

t

one or two tailed

is unknown one sample

Independent t

Test

two different independent groups

no no population

mean

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Name of Test Characteristics Formula

Correlatedt

Test

pre and post tests (pairs)

same group

Pearson’s r(Correlation)

measures the degree of relation between two variables

determines the degree of linear relations


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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, then you ALREADY possess the skills necessary to do statistics.

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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 cost resources to students and it provides professional statistical analysis and tools in a user friendly software environment for

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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.

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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

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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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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 supressed, data should be reported as collected.

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9. Misconduct

- Fabrication- Falsification- Plagiarism


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 Research “Ethics and Regulation” http://www.apa.org/science/research.htm

National Institutes of Health (NIH) “Bioethics Resources” http://www.nih.gov/sigs/bioethics/index.html

Research Ethicshttp://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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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 IRB s?

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 are detailed in the Common Rule as follows:

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a. informed consentb. protection of confidentiality or anonymity of all human subjectsc. acknowledging the right of the subject not to participate in a studyd. ensuring that subject is aware of his or her right to discontinue the

study at any time without adverse consequencee. ensuring that the study provides a benefit to the communityf. ensuring that the study has a direct benefit for the subject

participating in the studyg. ensuring that the subject is aware of the risks involved in the studyh. ensuring that the researcher has found less invasive or intrusive

ways to obtain the same informationi. that the individual subject has given permission to be deceived

during an experimental studyj. that parents have granted permission for children under the age of

18 to participatek. 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 risksm. specific measures for achieving each of the above has been spelled

outn. 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 publishedb. the study involves experimentation on human subjectsc. the study is invasive or intrusive in some wayd. the study involves deceptione. there are possible risks to the subjectf. there may be no community benefit or direct benefit for the

subjectg. there is a possible conflict of interest by researchers in the study

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h. medical or mental health research5. 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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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.

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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.

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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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 list-serve.

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 is 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.)

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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.

8. Schedule a time to write every day. Make it automatic! Thirty to ninety minutes per 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, and so

forth)

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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.

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 for

Research and Basic

Statistics

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Fundamental Terms 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

Anchor – a written descriptor for a point on a rating scale

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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

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Coefficient alpha – a variant of the Kuder-Richardson formula that provides an estimate of the reliability of a homogenous test

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

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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

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”

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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

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

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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

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

Diagraming – 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

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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

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

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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

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

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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

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

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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

Field notes – 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

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

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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

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

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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 measuredIntelligence – 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

Inter-coder reliability – consistency among different coders

Interim analysis – the cyclical process of collecting and analyzing data during a single research study

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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

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

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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

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

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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

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

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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

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

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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

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

Non-directional alternative hypothesis – an alternative hypothesis that includes the “not equal to” sign

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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

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

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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

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

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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

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

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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

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

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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

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

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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

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

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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

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

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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

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”

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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

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

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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

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

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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

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

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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

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

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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

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

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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

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

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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

Note: These are common terms and are not attributed to any one source.

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PART III:

Partial Listing of Selected References And Acknowledgements

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Partial Listing of Selected References and Acknowledgements


William Allan Kritsonis, PhD (2011)

Directories

American Educators Encyclopedia, 1991A 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

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Yearbooks and Handbooks

Educator’s Handbooks: A Research Perspective, 1987

International Handbook of Education Systems, Vol. 1: Europe and Canada.Vol. North Africa and the Middle East. Vol. 3: Asia, Australasian and Latin America, 1988

Statistical Yearbook/Annuaire/Statistique/Annuario Estadistico, 1984Comprehensive 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

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UNESCO Statistical Digest, education Reference X L 11 S863

World Education Report, 1991

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

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National FORUM of Educational Administration and Supervision Journal http://www.nationalforum.com/

National FORUM of Applied Educational Research Journalhttp://www.nationalforum.com/

National FORUM of Teacher Education Journalhttp://www.nationalforum.com/

National FORUM of Special Education Journalhttp://www.nationalforum.com/

On-Line Scholarly Electronic Journal Division of National FORUM Journals – Numerous national refereed periodicals.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

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Web Sites

American Demographics www.umich.edu/-nes

Bureau of Economic Analysis www.bea.doc.gov

Bureau of Labor Statistics www.stats.bis.govCondition of EducationNces.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 Researchwww.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 2009nces.ed.gov/pubsearch/pubsinfo.asp?pubid=1999038

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The Qualitative Report www.nove.edu/ssss/OR

Research and Statistics www.ed.gov/stats.html

Research Reports from The National Research and Development Centershttp://research.cse.ucla.edu

STAT-USA Internet www.stat-usa.govStatistical Abstracts of the United States www.census.gov/state_abstract

Statistical Resources on the Webwww.lib.umich.edu/libhome/Documents.Center/Stats.html

University of Michigan Documents Center: Statistics Sectionwww.lib.umich.edu/libhome/Documents.center

University of Virginia Social Science Data Centerwww.lib.virginia.edu/social/interactives.html

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

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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)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

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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 Materialswww.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

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.

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Charles, C. M. & Mertler, C. A. (2002). Introduction to Educational Research (4th Edition). Boston, MA: Allyn & Bacon.

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.

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 Author

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William Allan Kritsonis, PhD

William H. Parker Leadership Academy Hall of Honor

In 2008, Dr. Kritsonis was inducted into the William H. Parker Leadership Academy Hall of Honor, Graduate School, Prairie View A&M University – The Texas A&M University System. He was nominated by doctoral and master’s degree students.

Dr. Kritsonis Lectures at the University of Oxford, Oxford, England

In 2005, Dr. Kritsonis was an Invited Visiting Lecturer at the Oxford Round Table at Oriel College in the University of Oxford, Oxford, England. His lecture was entitled the Ways of Knowing Through the Realms of Meaning.

Dr. Kritsonis Recognized as Distinguished Alumnus

In 2004, Dr. William Allan Kritsonis was recognized as the Central Washington University Alumni Association Distinguished Alumnus for the College of Education and Professional Studies. Dr. Kritsonis was nominated by alumni, former students, friends, faculty, and staff. Final selection was made by the Alumni Association Board of Directors. Recipients are CWU graduates of 20 years or more and are recognized for achievement in their professional field and have made a positive contribution to society. For the second consecutive year, U.S. News and World Report placed Central Washington University among the top elite public institutions in the west. CWU was 12th

on the list in the 2006 On-Line Education of “America’s Best Colleges.”

Educational Background

Dr. William Allan Kritsonis earned his BA in 1969 from Central Washington University, Ellensburg, Washington. In 1971, he earned his M.Ed. from Seattle Pacific University. In 1976, he earned his PhD from the University of Iowa. In 1981, he was a Visiting Scholar at Teachers College, Columbia University, New York, and in 1987 was a Visiting Scholar at Stanford University, Palo Alto, California.

Doctor of Humane Letters

In June 2008, Dr. Kritsonis received the Doctor of Humane Letters, School of Graduate Studies from Southern Christian University. The ceremony was held at the Hilton Hotel in New Orleans, Louisiana.

Professional Experience

Dr. Kritsonis began his career as a teacher. He has served education as a principal, superintendent of schools, director of student teaching and field experiences,

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invited guest professor, author, consultant, editor-in-chief, and publisher. Dr. Kritsonis has earned tenure as a professor at the highest academic rank at two major universities.

Books – Articles – Lectures - Workshops

Dr. Kritsonis lectures and conducts seminars and workshops on a variety of topics. He is author of more than 600 articles in professional journals and several books. His popular book SCHOOL DISCIPLINE: The Art of Survival is scheduled for its fourth edition. He is the author of the textbook William Kritsonis, PhD on Schooling that is used by many professors at colleges and universities throughout the nation and abroad.

In 2009, Dr. Kritsonis coauthored the textbook A Statistical Journey: Taming of the Skew. The book has been adopted by professors in many colleges and universities throughout the nation. It was published by the Alexis/Austin Group, Murrieta, California.

In 2008-2009, Dr. Kritsonis coauthored the book Effective Teaching in the Elementary School. First year teachers, as well as seasoned educators will find the chapters of this book packed with practical and workable solutions to typical classroom problems.

In 2007, Dr. Kritsonis’ version of the book of Ways of Knowing Through the Realms of Meaning (858 pages) was published in the United States of America in cooperation with partial financial support of Visiting Lecturers, Oxford Round Table (2005). The book is the product of a collaborative twenty-four year effort started in 1978 with the late Dr. Philip H. Phenix. Dr. Kritsonis was in continuous communication with Dr. Phenix until his death in 2002.

In 2007, Dr. Kritsonis was the lead author of the textbook Practical Applications of Educational Research and Basic Statistics. The text provides practical content knowledge in research for graduate students at the doctoral and master’s levels. Dr. Kritsonis’ seminar and workshop on Writing for Professional Publication has been very popular with both professors and practitioners. Persons in attendance generate an article to be published in a refereed journal at the national or international levels. Dr. Kritsonis has traveled and lectured throughout the United States and world-wide. Some recent international tours include Australia, New Zealand, Tasmania, Turkey, Italy, Greece, Monte Carlo, England, Holland, Denmark, Sweden, Finland, Russia, Estonia, Poland, Germany, and many more.

Founder of National FORUM Journals – Over 4,000 Professors Published

Dr. Kritsonis is founder of NATIONAL FORUM JOURNALS (since 1983). These publications represent a group of highly respected scholarly academic periodicals. Over 4,000 writers have been published in these refereed, peer-reviewed periodicals. In 1983, he founded the National FORUM of Educational Administration and Supervision – now acclaimed by many as the United States’ leading recognized scholarly academic refereed journal in educational administration, leadership, and supervision. Over 5000 professionals have published in National FORUM Journals. In 1987, Dr. Kritsonis founded the National FORUM of Applied Educational Research Journal whose aim is to conjoin the efforts of applied educational researchers

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world-wide with those of practitioners in education. He founded the National FORUM of Teacher Education Journal, National FORUM of Special Education Journal, National FORUM of Multicultural Issues Journal, International Journal of Scholarly Academic Intellectual Diversity, International Journal of Management, Business, and Administration, and the DOCTORAL FORUM – National Journal for Publishing and Mentoring Doctoral Student Research, National FORUM Journal of Criminal Justice Studies, and SCHOOLING. The DOCTORAL FORUM is the only refereed journal in America committed to publishing doctoral students while they are enrolled in course work in their doctoral programs. In 1997, he established the Online Journal Division of National FORUM Journals that publishes academic scholarly refereed articles daily on the website: www.nationalforum.com. Over 900 professors have published online. In January 2007, Dr. Kritsonis established Focus: On Colleges, Universities, and Schools.

Professorial Roles

Dr. Kritsonis has served in professorial roles at Central Washington University, Washington; Salisbury State University, Maryland; Northwestern State University, Louisiana; McNeese State University, Louisiana; and Louisiana State University, Baton Rouge in the Department of Administrative and Foundational Services. In 2006, Dr. Kritsonis published two articles in the Two-Volume Set of the Encyclopedia of Educational Leadership and Administration published by SAGE Publications, Thousand Oaks, California. He is a National Reviewer for the Journal of Research on Leadership, University Council for Educational Administration (UCEA). In 2007, Dr. Kritsonis was invited to write a history and philosophy of education for the ABC-CLIO Encyclopedia of World History. Currently, Dr. Kritsonis is Professor of Educational Leadership at Prairie View A&M University – Member of the Texas A&M University System. He teaches in the PhD Program in Educational Leadership. Dr. Kritsonis taught the Inaugural class session in the doctoral program at the start of the fall 2004 academic year. In October 2006, Dr. Kritsonis chaired the first doctoral student to earn a PhD in Educational Leadership at Prairie View A&M University. He has chaired over 19 doctoral dissertations. He lives in Houston, Texas.

Copyright 2011 William Allan Kritsonis, PhDALL RIGHTS RESERVED/FOREVER

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