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# Education In Korea (Ppt)

## by David Deubelbeiss, Professor  at Schulich School of Education, Nipissing University on Sep 24, 2007

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• Ultracet http://www.fioricetsupply.com is the place to resolve the price problem. Buy now and make a deal for you. 4 years ago
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• Sharon Ha, Internet marketing & at Working full time very interesting, to know about your country. 5 years ago
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• Alysaally Education in korea is very nice. 5 years ago
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• Will1945 The Basics Understanding and Using Statistics by William Allan Kritsonis, PhD

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.

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 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.

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
5 years ago
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• Will1945 Getting Started With Research: Avoiding the Pitfalls by William Allan Kritsonis, PhD & David E. Herrington, PhD - PVAMU, The Texas A&M University System

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.

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.

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.
5 years ago
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• Will1945 Ethics and Research by William Allan Kritsonis, PhD & David E. Herrington, PhD -- PVAMU, The Texas A&M University System

1. Responsible conduct guiding researchers. Universities, federal and state government as well as professional organizations have guidelines on ethical behavior and research.

2. Informed consent - Participants must be informed and voluntarily give their consent to participate in a study.

- Participants must be fully informed about all procedures and possible risks.

- Participants informed of purpose of research and how data will be used.

- Benefits of study.

- Alternative treatments and potential compensation.

- They must understand and arrive at a decision without coercion. (Voluntary participation)

- Starts before the research begins.

- Privacy and confidentiality of research subjects and data .

- Contacts

- Approval of the IRB (Internal Review Board)

3. Termination of research if harm is likely. Risk-benefit assessments.

4. Special protection for vulnerable populations of research subjects.

5. Equitable recruitment of participants.

6. Results should be for the good of society and unattainable by any other means.

7. Beneficence - To promote understanding and shed light on the human condition. Protection of those participating in the study.

8. Honesty - No data to be suppressed, data should be reported as collected.

9. Misconduct

- Fabrication

- Falsification

- Plagiarism

SUGGESTED STUDENT ACTIVITIES:

1. In small groups discuss the relationship between academic freedom and research ethics. Share your discussion with the entire class.

2. What steps should researchers take to ensure all areas of informed consent are addressed in their research study? Share your discussion with the class.

3. What steps would you take to make sure you are not involved in unethical conduct in research? Share your discussion with the class.

WEBSITES

APA's Research “Ethics and Regulation”

http://www.apa.org/science/research.html

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

Research Ethics

http://faculty.ncwc.edu/toconnor/308/308lect10.htm

The National Institutes of Health (NIH) 'Human Participants Protections Education for Research Teams”

http://ethics.od.nih.gov/

The Department of Health and Human Services' (DHHS) Office of Research Integrity http://www.ori.hhs.gov/
5 years ago
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• Will1945 Ethics in Research on Human Subjects and the Role of the Institutional Review Board

Frequently Asked Questions by William Allan Kritsonis, PhD & David E. Herrington, PhD, PVAMU - The Texas A&M University System

1. What is an IRB?

The IRB is a committee that is assigned the task of reviewing proposed research by a university or other institution that receives federal funds and is in the business of conducting research on human subjects. The IRB is required by part 46 of Title 45 of the Code of Federal Regulations also called 45 CFR 46. According to the Department of Health and Human Services, it is the responsibility of the IRB to recommend to university officials that proposed research either be approved or disapproved based on a set of rules called the Common Rule.

2. Why do we have IRBs?

Every institution that conducts research on human subjects that also receives federal funds must provided a formal mechanism for ensuring that research is conducted in a manner that reflects nationally recognized standards. Failure to comply with policy can place the researcher and his institution at risk for litigation. In some a few instances the federal government has temporarily suspended all research activities at key research universities for failure to comply with the law.

3. What is the Common Rule

The Common Rule was established in 1991 in federal law 45 CFR 46.112. It details all of the areas of compliance with accepted norms for conducting research on human subjects established by the Helsinki Agreement and a series of declarations referred to as the Belmont Report. These principles re detailed in the Common Rule. These include:

a. informed consent

b. protection of confidentiality or anonymity of all human subjects

c. acknowledging the right of the subject not to participate in a study

d. ensuring that subject is aware of his or her right to discontinue the study at any time without adverse consequence

e. ensuring that the study provides a benefit to the community

f. ensuring that the study has a direct benefit for the subject participating in the study

g. ensuring that the subject is aware of the risks involved in the study

h. ensuring that the researcher has found less invasive or intrusive ways to obtain the same information

i. that the individual subject has given permission to be deceived during an experimental study

j. that parents have granted permission for children under the age of 18 to participate

k. that any psychological or physical harms will be remedied with expenses paid by the researchers.

l. the researcher is protected from possible harms or is taking informed risks

m. specific measures for achieving each of the above has been spelled out

n. that theses measures are meticulously followed.

4. Are all studies subject to IRB approval?

No. However all studies that will involve gathering data from the public or that will be published in some form must be reviewed before university officials will approve the protocol. To accommodate social science research and historical research expedited review protocols are submitted. Studies that must be reviewed meet the following criteria:

a. the results will be published

b. the study involves experimentation on human subjects

c. the study is invasive or intrusive in some way

d. the study involves deception

e. there are possible risks to the subject

f. there may be no community benefit or direct benefit for the subject

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

h. medical or mental health research

5. When my study has been approved by the IRB, are there any additional requirements that researchers must follow?

Yes. The Common Rule states that research approved by an IRB may be subject to further review for approval or disapproval by officials of the institution under the following circumstances:

a. if a third party complains of possible wrong-doing or harms realized

b. a senior administrator at the university may raise questions that would result in a follow-up IRB review.
5 years ago
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• Will1945 RESEARCH, WRITING & PUBLICATION by William Allan Kritsonis, PhD & David E. Herrington, PhD - PhD Program in Educational Leadership, PVAMU, Texas A&M University System.

1. Brainstorm ideas for research and possible publication.

- Look at current journals to see what is current or a “hot” topic. Many also have a “Call for Papers” listing the topics they plan to publish in future editions.

- Ask professional educational organizations what topics are popular or important issues in their field of education.

- Think about what interests you. You have to live with the topic until you complete it. If you are not interested in the topic, it will become boring or be difficult to keep on task and complete.

- Find out if a colleague or another person in the field of education has a project, interest, etc. that you could work on with them.

- Find out if a textbook company is looking for someone to write a chapter in a textbook. These might be on their website or they might send an email to those on their listserve.

2. Determine the type of manuscript you want to write. (NOTE: You are working on a manuscript. Many people call or interchange the term article for manuscript. A MANUSCRIPT is work that is submitted for possible publication. An ARTICLE is a manuscript that has been published.)

- Objective survey of the literature available on a topic

- Analysis of literature to support the author’s viewpoint

- Interpretive paper on a specific theory, concept, etc.

- Theory paper that develops a new conceptual framework

- Research paper - describing the study, participants, results, conclusions, etc.

- Chapter for a textbook (They are the easiest to be accepted since they do not have to go through a blind peer-review process)

- Other types of papers as indicated in the professional journals you read

3. It's also important to know what types of manuscripts a journal typically publishes.

- The library should have current issues for your review. Many can be found online.

- Review the types of article in several issues of the journal. Do they accept a variety of topics for publication or do they have a theme for the issue?

- Read the submission or author guidelines. Many can be found online.

- Look at the expertise of the members of the editorial board for ideas on their research interests.

4. The acceptance rates of journals can range from 80% to 5%. Look at publishing in journals where the turnaround time may be shorter. Journals which have very high submission rates have high rejection rates. Look at using your time wisely. Don’t “tie up” an article for 18 months if the journal has a low acceptance rate.

5. Ask colleagues which journals they have submitted manuscripts to. They can give good advice on the “where to” and “where not to” for submissions.

6. Determine which journal you will submit your manuscript. It is important to know where you are going to know how to begin the writing process. It is like taking a trip. You can have a well organized vacation by using a map or a “fly by the seat of your pants” experience without the map. You save time, energy and have a greater chance for successful publication by knowing where you are going. (Remember research ethics. Only submit your manuscript to one journal at a time. You can submit to another journal if you receive notice that your manuscript will not be published by the editor.)

7. When possible, collaborate in writing! A group of two or more can share ideas and the work.

- Decide on the topic

- Decide the role and responsibility of each team member. (Use each other’s talents. Some are better at writing, others at finding the references, others at editing, etc.)

- Set timelines

- Meet on a regular basis to keep each other on task, and make changes as needed.

8. Schedule a time to write every day. Make it automatic! Thirty to ninety minutes a day, or at least three times a week. This will help you to stay on target and not get overwhelmed at the last minute when your writing project is due.

9. Develop an outline for your manuscript. You can read the published articles in the journal where you plan to submit and determine what type of outline to develop.

10. Write your introduction and summary first. Most problems are found in these sections. They become a guide to your manuscript (a roadmap)! It will keep you focused on the route you are taking.

11. As you write make sure the manuscript indicate you know what is current on that topic. Make sure to have at least one to two references from the same year you plan to submit your manuscript.

12. Make sure your manuscript has a solid conceptual basis.

13. Make sure that findings in your conclusion have been substantiated in your paper.

14. When the paper is well organized and near completion have a couple of colleagues review and edit it.

- Does it make sense to someone else who has read it?

- Does it follow the publication style? (APA, Chicago, MLA, etc.)

15. Tips for submitting your manuscript after it is completed:

- Make sure you have the exact copies required.

- Write a cover letter with the current editor’s name.

- The cover letter should be neat and a brief description of your manuscript, why you are submitting it and your contact information.

- If an online submission, are all guidelines for submission followed?

- If mailing the manuscript, make sure you have the post office weigh the envelope so you can buy the correct amount for postage.

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.

- 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.
5 years ago
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• Will1945 Fundamental Terms in Educational Research

and Basic Statistics - Complied by William Allan Kritsonis, PhD, Professor (Tenured) PVAMU, Texas A&M University System

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

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

Causal modeling – a form of explanatory research where the researcher hypothesizes a causal model and then empirically tests the model. Also called structural equation modeling or theoretical modeling.

Causal-comparative research – a form of non-experimental research where the primary independent variable of interest is categorical

Cause and effect relationship – when one variable affects another variable

Cell – a combination of two or more independent variables in a factorial design

Census – a study of the whole population rather than a sample

Changing-criterion design – a single-case experimental design in which a participant’s behavior is gradually altered by changing the criterion for success during successive treatment periods

Checklist – a list of response categories that respondents check if appropriate

Chi square test for contingency tables – statistical test used to determine if a relationship observed in a contingency table is statistically significant

CIJE – an annotated index of articles from educational journals

Closed-ended question – a question that forces participants to choose a response

Cluster -- a collective type of unit that includes multiple elements

Cluster sampling – type of sampling where clusters are randomly selected

Co-occurring codes – sets of codes that partially or completely overlap

Coding – marking segments of data with symbols, descriptive words, or category names

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

Cohort – any group of people with a common classification or common characteristic

Cohort study – longitudinal research focusing specifically on one or more cohorts

Collective case study – studying multiple cases in one research study

Complete participant – researcher becomes member of group being studied and does not tell members they are being studied

Complete observer – researcher observes as an outsider and does not tell the people they are being observed

Comprehensive sampling – including all cases in the research study

Concurrent validity – validity evidence obtained from assessing the relationship between test scores and criterion scores obtained at the same time

Confidence interval – a range of numbers inferred from the sample that has a certain probability of including the population parameter

Confidence limits – the endpoints of a confidence interval

Confidentiality – not revealing the identity of the participant to anyone other than the researcher and the researcher’s staff

Confounding variable – an extraneous variable that systematically varies with the independent variable and also influences the dependent variable

Constant – a single value or category of a variable

Constant comparative method – data analysis in grounded theory research

Construct validity – evidence that a theoretical construct can be inferred from the scores on a test

Construct – an informed, scientific idea developed or “constructed” to describe or explain behavior

Content validity – a judgment of the degree to which the items, tasks, or questions on a test adequately sample the domain of interest

Contextualization – the identification of when and where an event took place

Contingency table – a table displaying information in cells formed by the intersection of two or more categorical variables

Control group – the group that does not receive the experimental treatment condition

Convenience sampling – people who are available or volunteer or can be easily recruited are included in the sample

Convergent evidence – evidence that the scores on prior tests and the current test designed to measure the same construct are correlated

Correlation coefficient – an index indicating the strength and direction of relationship between two variables

Correlational research – a form of non-experimental research where the primary independent or predictor variable of interest is quantitative

Corroboration – comparing documents to each other to determine whether they provide the same information or reach the same conclusion

Counterbalancing – administering the experimental treatment conditions to all comparison groups, but in a different order

Criterion of falsifiability – statements and theories should be “refutable”

Criterion-related validity – a judgment of the extent to which scores from a test can be used to predict or infer performance in some activity

Critical case sampling – selecting what are believed to be particularly important cases

Cronbach’s alpha – see coefficient alpha

Cross-sectional research – data are collected at a single point in time

Culture – a system of shared beliefs, values, practices, perspectives, folk knowledge, language, norms, rituals, and material objects and artifacts that the members of a group use in understanding their world and in relating to others

Data set – a set of data

Data triangulation – the use of multiple data sources

Debriefing – a post study interview in which all aspects of the study are revealed, any reasons for deception are explained, and any questions the participant has about the study are answered

Deception – misleading or withholding information from the research participant

Deductive reasoning – drawing a specific conclusion from a set of premises

Deductive method – a top down or confirmatory approach to science

Dehoaxing – informing participants about any deception used and the reasons for its use

Deontological approach – an ethnical approach that says ethical issues must be judged on the basis of some universal code

Dependent variable – a variable that is presumed to be influenced by one or more independent variables

Description – attempting to describe the characteristics of a phenomenon

Descriptive validity – the factual accuracy of an account as reported by the researcher

Descriptive research – research focused on providing an accurate description or picture of the status or characteristics of a situation or phenomenon

Descriptive statistics – division of statistics focused on describing, summarizing, or making sense of a particular set of data

Desensitizing – reducing or eliminating any stress or other undesirable feelings the participant may have as a result of participating in the study.

Determinism – the assumption that all events have causes

Diagnostic tests – tests designed to identify where a student is having difficulty with an academic skill

Diagramming – making a sketch, drawing, or outline to show how something works or to clarify the relationship between the parts of a whole

Differential attrition – when participants do not drop out randomly

Differential influence – when the influence of an extraneous variable is different for the various comparison groups

Direct effect – the effect of the variable at the origin of an arrow on the variable at the receiving end of the arrow

Directional alternative hypothesis – an alternative hypothesis that contains either a “greater than” sign or a “less than” sign

Discriminant evidence – evidence that the scores on the newly developed test are not correlated with the scores on tests designed to measure theoretically different constructs

Disproportional stratified sampling – type of stratified sampling where the sample proportions are made to be different from the population proportions on the stratification variable

Double negative – a sentence construction that includes two negatives

Double-barreled question – a question that combines two or more issues or attitude objects

Duplicate publication – publishing the same data and results in more than one journal or in other publications

Ecological validity – the ability to generalize the study results across settings

Effect size indicator – a statistical measure of the strength of a relationship

Element – the basic unit that is selected from the population

Emic term – a special word or term used by the people in a group

Emic perspective – the insider’s perspective

Empirical – based on observation or experience

Empiricism – idea that knowledge comes from experience

Enumeration – the process of quantifying data

Equal probability selection method – any sampling method where each member of the population has an equal chance of being selected

Equivalent-forms reliability – a measure of the consistency of a group of individuals’ scores on two equivalent forms of a test measuring the same construct

ERIC – a database containing information from CIJE and RIE

Essence – the invariant structure of the experience

Ethical skepticism – an ethical approach that says concrete and inviolate moral codes cannot be formulated

Ethnocentrism – judging people from a different culture according to the standards of your own culture

Ethnography – the discovery and comprehensive description of the culture of a group of people; it’s a form of qualitative research focused on describing the culture of a group of people

Ethnohistory – the study of the cultural past of a group of people

Ethnology – the comparative study of cultural groups

Etic term – outsider’s words or special words that are used by social scientists

Etic perspective – an external, social scientific view of reality

Evaluation – determining the worth, merit, or quality of an evaluation object

Event sampling – observing only after specific events have occurred

Exhaustive categories – a set of categories that classify all of the relevant cases in the data

Exhaustive – property that response categories or intervals include all possible responses

Expectancy data – data illustrating the number or percentage of people that fall into various categories on a criterion measure

Experiment – an environment in which the researcher objectively observes phenomena that are made to occur in a strictly controlled situation in which one or more variables are varied and the others are kept constant

Experimental group – the group that receives the experimental treatment condition

Experimental control – eliminating any differential influence of extraneous variables

Experimenter effect – the unintentional effect that the researcher can have on the outcome of a study

Explanation – attempting to show how and why a phenomenon operates as it does

Explanatory research – testing hypotheses and theories that explain how and why a phenomenon operates as it does

Exploration – attempting to generate ideas about phenomena

Extended fieldwork – collecting data in the field over an extended period of time

External validity – the extent to which the study results can be generalized to and across populations of persons, settings and times

External criticism – determining the validity, trustworthiness, or authenticity of the source

Extraneous variable – A variable that may compete with the independent variable in explaining the outcome; any variable other than the independent variable that may influence the dependent variable

Extreme case sampling – identifying the “extremes” or poles of some characteristic and then selecting cases representing these extremes for examination

Facesheet codes – codes that apply to a complete document or case

Factor analysis – a statistical procedure that identifies the minimum number of “factors,” or dimensions, measured by a test

Factorial design – based on a mixed model – a factorial design in which different participants are randomly assigned to the different levels of one independent variable but all participants take all levels of another independent variable; it’s a design in which two or more independent variables are simultaneously studied to determine their independent and interactive effects on the dependent variable

Fieldnotes – notes taken by the observer

Filter question – an item that directs participants to different follow-up questions depending on the response

Focus group – a moderator leads a discussion with a small group of people

Formative evaluation – evaluation focused on improving the evaluation object

Frequency distribution – arrangement where the frequencies of each unique data value is shown

Front stage behavior – what people want or allow us to see

Fully anchored rating scale – all points are anchored on the rating scale

General linear model – a mathematical procedure that is the “parent” of many statistical techniques

Generalize – making statements about a population based on sample data

Going native – identifying so completely with the group being studied that you can no longer remain objective

Grounded theory – a general methodology for developing theory that is grounded in data systematically gathered and analyzed; a qualitative research approach

Group moderator -- the person leading the focus group discussion

Group frequency distribution – the data values are clustered or grouped into separate intervals and the frequencies of each interval is given Heterogeneous – a set of numbers with a great of variability

Historical research – the process of systematically examining past events or combinations of events to arrive at an account of what happened in the past History – any event, other than a planned treatment event that occurs between the pre- and post measurement of the dependent variable and influences the post measurement of the dependent variable

Holistic description – the description of how members of groups make up a group

Homogeneity – in test validity, refers to how well a test measures a single construct

Homogeneous sample selection – selecting a small and homogeneous case or set of cases for intensive study

Homogeneous – a set of numbers with little variability

Hypothesis – a prediction or educated guess

Hypothesis – a prediction or guess of the relation that exists among the variables being investigated

Hypothesis testing – the branch of inferential statistics concerned with how well the sample data support a null hypothesis and when the null hypothesis can be rejected In-person interview – an interview conducted face to face

Independent variable – a variable that is presumed to cause a change in another variable

Indirect effect – an effect occurring through an intervening variable

Inductive reasoning – reasoning from the particular to the general

Inductive codes – codes generated by a researcher by directly examining the data

Inductive method – a bottom up or generative approach to science

Inferential statistics – division of statistics focused on going beyond the immediate data and inferring the characteristics of population based on samples

Inferential statistics – use of the laws of probability to make inferences and draw statistical conclusions about populations based on sample data

Influence – attempting to apply research to change behavior

Informal conversational interview – spontaneous, loosely structured interview

Instrumental case study – interest is in understanding something more general than the particular case

Instrumentation – any change that occurs in the way the dependent variable is measured

Intelligence – the ability to think abstractly and to learn readily from experience

Inter-scorer reliability – the degree of agreement between two or more scorers, judges, or raters

Interaction with selection – occurs when the different comparison groups are affected differently by one of the threats to internal validity

Interaction effect – when the effect of one independent variable depends on the level of another independent variable

Intercoder reliability – consistency among different coders

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

Internal consistency – the consistency with which a test measures a single construct

Internal validity – the ability to infer that a causal relationship exists

Internal criticism – the reliability or accuracy of the information contained in the sources collected

Internet – a network of millions of computers joined to promote communication

Interpretive validity – accurately portraying the meaning given by the participants to what is being studied

Interrupted time-series design – a design in which a treatment condition is assessed by comparing the pattern of posttest responses obtained from a single group of participants

Interval scale – a scale of measurement that has equal intervals of distances between adjacent numbers

Intervening variable – a variable occurring between two other variables in a causal chain

Interview – a data collection method where interviewer asks interviewee questions

Interview guide approach – specific topics and/or open-ended questions are asked in any order

Interview protocol – data collection instrument used in an interview

Interviewee – the person being asked questions

Interviewer – the person asking the questions

Intracoder reliability – consistency within a single individual

Intrinsic case study – interest is in understanding a specific case

Investigator triangulation – the use of multiple investigators in collecting and interpreting the data

IRB – the institutional review committee that assesses the ethical acceptability of research proposals

Item stem – the set of words forming a question or statement Kuder-Richardson formula 20 – a statistical formula used to compute an estimate of the reliability of a homogeneous test

Laboratory observation – observation done in a lab or other setting set up by the researcher

Leading question – a question that suggests a researcher is expecting a certain answer

Level of confidence – the probability that a confidence interval to be constructed from a random sample will include the population parameter

Life-world – an individual’s inner world of immediate experience

Likert scale – a summated rating scale

Line graph – a graph that relies on the drawing of one or more lines

Logic of significance testing – understanding and following the logical

Longitudinal research – data are collected at multiple time points and comparisons are made across time

Low-inference descriptors – description phrased very close to the participants’ accounts and the researchers’ field notes

Lower limit – the smallest number on a confidence interval

Main effect – the effect of one independent variable

Manipulation – an intervention studied by an experimenter

Margin of error – one half of the width of a confidence interval

Master list – a list of all the codes used in a research study

Maturation – any physical or mental change that occurs over time that affects performance on the dependent variable

Maximum variation sampling – purposively selecting a wide range of cases

Mean – the arithmetic average

Measure of relative standing – provides information about where a score falls in relation to the other scores in the distribution of data

Measure of central tendency – the single numerical value that is considered the most typical of the values of a quantitative variable

Measure of variability – a numerical index that provides information about how spread out or how much variation is present

Measurement – the act of measuring by assigning symbols or numbers to something according to a specific set of rules

Median – the 50th percentile

Median location – the numerical place where you can find the median in a set of order numbers

Mediating variable – an intervening variable

Memoing – recording reflective notes about what you are learning from the data

Mental Measurements Yearbook – one of the primary sources of information about published tests

Meta-analysis – a quantitative technique used to integrate and describe the results of a large number of studies

Method of working hypotheses – attempting to identify all rival explanations

Method of data collection – technique for physically obtaining data to be analyzed in a research study

Methods triangulation – the use of multiple research methods

Mixed purposeful sampling – the mixture of more than one sampling strategy

Mode – the most frequently occurring number

Moderator variable – a variable involved in an interaction effect; see interaction effect

Mortality – A differential loss of participants from the various comparison groups

Multigroup research design – a research design that includes more than one group of participants

Multimethod research – the use of more than one research method

Multiple operationalism – the use of several measures of a construct

Multiple regression – regression based on one dependent variable and two or more independent variables

Multiple time-series design – an interrupted time-series design that includes a control group to rule out a history effect

Multiple-baseline design – a single-case experimental design in which the treatment condition is successively administered to different participants, or to the same participant in several settings, after baseline behaviors have been recorded for different periods of time

Multiple-treatment interference -- occurs when participation in one treatment condition influences a person’s performance in another treatment condition

Mutually exclusive – property that categories or intervals do not overlap

Mutually exclusive categories – a set of categories that are separate or distinct

n – the recommended sample size

N – the population size

Naturalistic observation – observation done in “real world” settings

Naturalistic generalization – generalizing based on similarity

Negative criticism – Establishing the reliability or authenticity and accuracy of the content of the documents and other sources used by the researcher

Negative case sampling – selecting cases that disconfirm the researcher’s expectations and generalizations

Negative correlation – two variables move in opposite directions

Negative-case sampling – locating and examining cases that disconfirm the researcher’s expectations

Negatively skewed – skewed to the left

Network diagram – a diagram showing the direct links between variables or events over time

Nominal scale – a scale of measurement that uses symbols or numbers to label, classify, or identify people or objects

Nondirectional alternative hypothesis – an alternative hypothesis that includes the “not equal to” sign

Normal distribution – a unimodal, symmetric, bell-shaped distribution that is the theoretical model of many variables

Norms – the written and unwritten rules that specify appropriate group behavior

Null hypothesis – a statement about a population parameter

Numerical rating scale – a rating scale with anchored endpoints

Observation – unobtrusive watching of behavioral patterns

Observer-as-participant – researcher spends limited amount of time observing group members and tells members they are being studied

Official documents – anything written or photographed by an organization

One-group pretest-posttest design – a research design in which a treatment condition is administered to one group of participants after pretesting, but before posttesting on the dependent variable

One-group pretest-posttest design – administering a posttest to a single group of participants after they have been given an experimental treatment condition

One-group posttest-only design – administering a posttest to a single group of participants after they have been given an experimental treatment condition

One-stage cluster sampling – a set of clusters is randomly selected and all of the elements in the selected clusters are included in the sample

One-way analysis of variance – statistical test used to compare two or more group means

Open coding – the first stage in grounded theory data analysis

Open-ended question – a question that allows participants to respond in their own words

Operationalism – representing constructs by a specific set of steps or operations

Opportunistic sampling – selecting cases where the opportunity occurs

Oral histories – based on interviews with a person who has had directed or indirect experience with or knowledge of the chosen topic

Order effect – a sequencing effect that occurs from the order in which the treatment conditions are administered

Ordinal scale – a rank-order scale of measurement

Outlier – a number that is very atypical of the other numbers in a distribution

Panel study – study where the same individuals are studied at successive points over time

Parameter – a numerical characteristic of a population

Partial correlation – used to examine the relationship between two quantitative variables controlling for one or more quantitative extraneous variables

Partial publication – publishing several articles from the data collected in one large study; is generally not unethical for large studies

Participant feedback – discussion of the researcher’s conclusions with the actual participants

Participant-as-observer – researcher spends extended time with the group as an insider and tells members they are being studied

Path coefficient – a quantitative index providing information about a direct effect

Pattern matching – predicting a pattern of results and determining if the actual results fit the predicted pattern

Peer review – discussing one’s interpretations and conclusions with one’s peers or colleagues

Percentile ranks – scores that divide a distribution into 100 equal parts

Percentile rank – the percentage of scores in a reference group that fall below a particular raw score

Periodicity – the presence of a cyclical pattern in the sampling frame

Personal documents – anything written or photographed for private purposes

Personality – a multifaceted construct that does not have a generally agreed on definition

Phenomenology – the description of one or more individuals’ consciousness and experience of a phenomenon

Pilot test – a preliminary test of your questionnaire

Point estimate – the estimated value of a population parameter

Point estimation – the use of the value of a sample statistic as the estimate of the value of a population parameter

Population – the complete set of cases; it’s the large group to which a researcher wants to generalize the sample results

Population validity – the ability to generalize the study results to the individuals not included in the study

Positive correlation – two variables move in the same direction

Positive criticism – ensuring that the statements made or the meaning conveyed in the various sources is correct

Positively skewed – skewed to the right

Post hoc fallacy – making the argument that because A preceded B, A must have caused B

Post hoc test – a follow-up test to the analysis of variance

Posttest-only control-group design – administering a posttest to two randomly assigned groups of participants after one group has been administered the experimental treatment condition

Practical significance – a conclusion made when a relationship is strong enough to be of practical importance

Prediction – attempting to predict or forecast a phenomenon

Predictive research – research focused on predicting the future status of one or more dependent variables based on one or more independent variables

Predictive validity – validity evidence obtained from assessing the relationship between test scores collected at one point in time and criterion scores obtained at a later time

Presence or absence technique – manipulating the independent variable by presenting one group the treatment condition and withholding it from the other group

Presentism – the assumption that the present-day connotations of terms also existed in the past

Pretest-posttest control-group design – a research design that administers a posttest to two randomly assigned groups of participants after both have been pretested and one of the groups has been administered the experimental treatment condition

Primary source – a source in which the creator was a direct witness or in some other way directly involved or related to the event

Primary data – original data collected as part of a research study

Probabilistic cause – changes in variable A “tend” to produce changes in variable B; it’s a cause that usually produces an outcome

Probability value – the probability of the result of your research study, or an even more extreme result, assuming that the null hypothesis is true

Probability proportional to size – a type of two-stage cluster sampling where each cluster’s chance of being selected in stage one depends on its population size

Probe – prompt to obtain response clarity or additional information

Problem of induction – things that happened in the past may not happen in the future

Problem – an interrogative sentence that asks about the relation that exists between two or more variables

Proportional stratified sampling – type of stratified sampling where the sample proportions are made to be the same as the population proportions on the stratification variables

Prospective study – another term applied to a panel study

Purposive sampling – the researcher specifies the characteristics of the population of interest and locates individuals with those characteristics

Qualitative observation – observing all potentially relevant phenomena

Qualitative research – research relying primarily on the collection of qualitative data

Quantitative interview – an interview providing qualitative data

Quantitative observation – standardized observation

Quantitative variable – a variable that varies in degree or amount

Quantitative research – research relying primarily on the collection of quantitative data

Quasi-experimental research design – an experimental research design that does not provide for full control of potential confounding variables primarily by not randomly assigning participants to comparison groups

Questionnaire – a self-report data collection instrument filled out by research participant

Quota sampling – the researcher determines the appropriate sample sizes or quotas for the groups identified as important and takes convenience samples from these groups

Random assignment – randomly assigning a set of people to different groups; it’s a statistical control procedure that maximizes the probability that the comparison groups will be equated on all extraneous variables

Range – the difference between the highest and lowest numbers

Ranking – the ordering of responses into ranks

Rating scale – a continuum of response choices

Ratio scale – a scale of measurement that has a true zero point as well as all the characteristics of the nominal, ordinal, and interval scales

Rationalism – idea that reason is the primary source of knowledge

Reactivity – an alteration in performance that occurs as a result of being aware of participating in a study; it refers to changes occurring in people because they know they are being observed

Reference group – the norm group used to determine the percentile ranks

Reflexivity – self-reflection by the researcher on his or her biases and predispositions

Regression analysis – a set of statistical procedures used to predict the values of a dependent variable based on the values of one or more independent variables

Regression coefficient – the predicted change in Y given a one-unit changes in X

Regression line – the line that best fits a pattern of observations

Regression equation – the equation that defines the regression line

Reliability – consistency or stability

Repeated sampling – drawing many or all-possible samples from a population

Repeated-measures design – a design in which all participants participate in all experimental treatment conditions

Replication logic – the idea that the more times a research finding is shown to be true with different sets of people, the more confidence we can place in the finding and in generalizing beyond the original participants

Replication – research examining the same variables with different people

Representative sample – a sample that resembles the population

Research design – the outline, plan, or strategy used to answer a research question

Research ethics – a set of principles to guide and assist researchers in deciding which goals are most important and in reconciling conflicting values

Research hypothesis – the hypothesis of interest to the researcher and the one he or she would like to see supported by the study results

Research method – overall research design and strategy

Research plan – the outline or plan that will be used in conducting the research study

Research problem – see problem

Researcher bias – obtaining results consistent with what the researcher wants to find

Researcher-as-detective – metaphor applied to researcher when searching for cause and effect

Response rate – the percentage of people in a sample that participate in a research study

Response set – tendency to respond in a specific direction regardless of content

Retrospective research – the researcher starts with the dependent variable and moves backward in time

Retrospective questions – questions asking people to recall something from an earlier time

RIE – an index of abstracts of research reports

Rule of parsimony – selecting the most simple theory that works

Sample – the set of elements taken from a larger population

Sampling error – the difference between the value of a sample statistic and a population parameter

Sampling frame – a list of all the elements in a population

Sampling with replacement – it is possible for elements to be selected more than once

Sampling without replacement – it is not possible for elements to be selected more than once

Sampling interval – the population size divided by the desired sample size; it is symbolized by “k”

Sampling distribution – the theoretical probability distribution of the values of a statistic that results when all possible random samples of a particular size are drawn from a population

Sampling error – the difference between a sample statistic and the corresponding population parameter

Sampling distribution of the mean – the theoretical probability distribution of the means of all possible random samples of a particular size drawn from a population

Scatterplot – a graph used to depict the relationship between two quantitative variables

Science – an approach for the generation of knowledge

Secondary data – data originally collected at an earlier time by a different person for a different purpose

Secondary source – a source that was created from primary sources, secondary sources, or some combination of the two

Segmenting – dividing data into meaningful analytical units

Selection – selecting participants for the various treatment groups that have different characteristics

Selection by history interaction – occurs when the different comparison groups experience a different history event

Selection by maturation interaction – occurs when the different comparison groups experience a different rate of change on a maturation variable

Selection-maturation effect – when participants in one of two comparison groups grow or develop faster than participants in the other comparison group

Selective coding – the final stage in grounded theory data analysis

Semantic differential – a scaling technique where participants rate a series of objects or concepts

Sequencing effects – biasing effects that can occur when each participant must participate in each experimental treatment condition

Shared values – the culturally defined standards about what is good or bad or desirable or undesirable

Shared beliefs – the specific cultural conventions or statements that people who share a culture hold to be true or false

Significance level – the cutoff the researcher uses to decide when to reject the null hypothesis

Significance testing – a commonly used synonym for hypothesis testing

Simple random sample – a sample drawn by a procedure where every member of the population has an equal chance of being selected

Simple case – when there is only one independent variable and one dependent variable

Simple random sampling – the term usually used for sampling without replacement

Simple case of correlational research – when there is one quantitative independent variable and one quantitative dependent variable

Simple regression – regression based on one dependent variable and one independent variable

Simple case of causal-comparative research – when there is one categorical independent variable and one quantitative dependent variable

Single-case experimental designs – designs that use a single participant to investigate the effect of an experimental treatment condition

Skewed – not symmetrical

Snowball sampling – each research participant is asked to identify other potential research participants

Social desirability response set – tendency to provide answers that are socially desirable

Sourcing – information that identifies the source or attribution of the document

Spearman-Brown formula – a statistical formula used for correcting the split-half reliability coefficient for the shortened test length created by splitting the full-length test into two equivalent halves

Split-half reliability – a measure of the consistency of the scores obtained from two equivalent halves of the same test

Spurious relationship – when the relationship between two variables is due to one or more third variables

Standard error – the standard deviation of a sampling distribution

Standard deviation – the square root of the variance

Standard scores – scores that have been converted from one scale to another to have a particular mean and standard deviation

Standardization – presenting the same stimulus to all participants

Standardized open-ended interview – a set of open-ended questions are asked in a specific order and exactly as worded

Starting point – a randomly selected number between one and k

States – distinguishable, but less enduring ways in which people differ

Static-group comparison design – comparing posttest performance of a group of participants who have been given an experimental treatment condition with a group that has not been given the experimental treatment condition

Statistic – a numerical characteristic of a sample

Statistical regression – the tendency of very high scores to become lower and very low scores to become higher on post testing

Statistically significant – a research finding is probably not attributable to chance; it’s the claim made when the evidence suggests an observed result was probably not due to chance

Stratification variable – the variable on which the population is divided

Stratified sampling – dividing the population into mutually exclusive groups and then selecting a random sample from each group

Structural equation modeling – see causal modeling

Summated rating scale – a multi-item scale that has the responses for each person summed into a single score

Summative evaluation – evaluation focused on determining overall effectiveness of the evaluation object

Survey research – a term sometimes applied to non-experimental research based on questionnaires or interviews

Synthesis – the selection, organization and analysis of the materials collected

Systematic sample – a sample obtained by determining the sampling interval, selecting a random starting point between 1 and k, and then selecting every kith element

t test for correlation coefficients – statistical test used to determine if a correlation coefficient is statistically significant

t test for independent samples – statistical test used to determine if the difference between the means of two groups is statistically significant

t test for regression coefficients – statistical test used to determine if a regression coefficient is statistically significant

Table of random numbers – a list of numbers that fall in a random order

Target population – the larger population to whom the study results are to be generalized

Telephone interview – an interview conducted over the phone

Temporal validity – The extent to which the study results can be generalized across time

Test-retest reliability – a measure of the consistency of scores over time

Testing – any change in scores obtained on the second administration of a test as a result of having previously taken the test

Tests in Print – A primary source of information about published tests

Theoretical sensitivity – when a researcher is effective at thinking about what kinds of data need to be collected and what aspects of already collected data are the most important for the grounded theory

Theoretical validity – the degree to which a theoretical explanation fits the data

Theoretical saturation – occurs when no new information or concepts are emerging from the data and the grounded theory has been validated

Theory – an explanation or an explanatory system; a generalization or set of generalizations used systematically to explain some phenomenon

Theory triangulation – the use of multiple theories and perspectives to help interpret and explain the data

Think-aloud technique – has participants verbalize their thoughts and perceptions while engaged in an activity

Third variable – a confounding extraneous variable

Third variable problem – an observed relationship between two variables may be due to an extraneous variable

Three necessary conditions – three things that must be present if you are to contend that causation has occurred

Time interval sampling – checking for events during specific time intervals

Transcription – transforming qualitative data into typed text

Trend study – independent samples are taken from a population over time and the same questions are asked

Two-stage cluster sampling – first a set of clusters is randomly selected and second a random sample of elements is drawn from each of the clusters selected in stage one

Type I error – rejecting a true null hypothesis

Type II error – failing to reject a false null hypothesis

Type technique – manipulating the independent variable by varying the type of variable presented to the different comparison groups

Typical case sampling – selecting what are believed to be average cases

Typology – a classification system that breaks something down into different types or kinds

Unrestricted sampling – the technical term used for sampling with replacement

Upper limit – the largest number on a confidence interval

Utilitarianism – an ethical approach that says judgments of the ethics of a study depend on the consequences the study has for the research participants and the benefits that may arise from the study

Vagueness – uncertainty in the meaning of words or phrases

Validation – the process of gathering evidence that supports and inference based on a test score or scores

Validity coefficient – a correlation coefficient computed between test scores and criterion scores

Validity – a judgment of the appropriateness of the interpretations, inferences, and actions made on the basis of a test score or scores

Variable – a condition or characteristic that can take on different values or categories

Variance – a measure of the average deviation from the mean in squared units

Y-intercept – the point where the regression line crosses the Y-axis

z-score – a raw score that has been transformed into standard deviation units
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• Will1945 National Researchists - Book by William Allan Kritsonis, PhD and Colleagues

________________________________________

William Allan Kritsonis, PhD is Editor-in-Chief of the NATIONAL FORUM JOURNALS. He is a tenured professor in the PhD Program in Educational Leadership at Prairie View A&M University/Member Texas A&M University System. He was a Visiting Lecturer (2005) at the Oxford Round Table, Oriel College in the University of Oxford, Oxford, ENGLAND. Dr. Kritsonis is also a Distinguished Alumnus (2004) at Central Washington University in the College of Education and Professional Studies, Ellensburg, Washington. He has authored or co-authored numerous articles and conducted several research presentations with students and colleagues in the field of education. Dr. Kritsonis has served education as a school principal, superintendent of schools, director of field experiences and student teaching, consultant, and professor.

Kimberly Grantham Griffith, Ph.D., is Editor of THE LAMAR UNIVERSITY ELECTRONIC JOURNAL OF STUDENT RESEARCH. She is a tenured associate professor in the Department of Professional Pedagogy at Lamar University/Member Texas State University System. Dr. Griffith is also a Councilor (board member) for the At-Large Division, Council for Undergraduate Research (CUR). In April 2000, she received the prestigious Lamar University Merit Award for teaching excellence. Dr. Griffith serves on the editorial board of the Electronic Journal of Inclusive Education. She has co-authored numerous articles and conducted several research presentations with students and colleagues in the field of education.

Cristian Bahrim, Ph.D., is an assistant professor in the Department of Chemistry and Physics at Lamar University and holds a joint-appointment in the Department of Electrical Engineering. He is (co-)author in several papers published in peer-reviewed journals/books and conferences’ proceedings. He conducted several research projects in the field of atomic physics, optics, lasers, astronomy and physics education. Since 2001, Dr. Bahrim has served as reviewer for the Journal of Physics of the Institute of Physics (England), and recently he joined the editorial board of “The Lamar University Electronic Research Journal of Student Research”. Dr. Bahrim received the M.S. degree in Physics from University of Bucharest in 1991 and the Ph.D. degree in Physics from University of Paris in 1997. He held a research associate position in Kansas State University (1999-2001) and he was research assistant in the Institute of Atomic Physics, Romania (1991-1998). He obtained two outstanding McNair Mentor awards in 2005. Since 2000, he was selected in several Marquis Who’s Who publications. Dr. Bahrim was the recipient of a French Government Scholarship (1991-1996).

David E. Herrington, Ph.D., is an assistant professor in the Department of Educational Leadership and Counseling at Prairie View A&M University/Member Texas A&M University System. He has supervised more than 2,000 student research field-based projects. Dr. Herrington has co-authored numerous articles with students in the area of education. He believes that everyone uses statistical thinking and inductive reasoning in everyday life. Making students aware of their existing skills and knowledge in these areas provides them with a sense that, in many ways, statistical reasoning and scientific processes are familiar. The value of a statistics course comes from the development of the specialized vocabulary, participative data gathering methods, and data analysis techniques that can enhance or leverage existing conceptual frameworks that students bring into the learning process.

Robert L. Marshall, EdD is the Senior National Editor of NATIONAL FORUM JOURNALS. He is a tenured professor at Western Illinois faculty in the Educational Leadership Department. His background in education spans over 25 years and includes teaching in secondary public schools, campus as well as district level administrative experience and ten year in higher education as a professor of Educational Leadership in the Texas A&M University System. Dr. Marshall's research interests are in the areas of distance education, secondary student success initiatives along with studies related to the principalship and superintendency in public schools.

Copyright  2009 William Allan Kritsonis, Ph.D.

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## Education In Korea (Ppt)Presentation Transcript

•
•
• Introduction of modern education system
• 1880s
• 1940s
• 1950s
: introduced the first modern school system : established a modern education system : introduced compulsory education (Wonsan Academy 1883, Baejae Academy 1885) (single track system 6-3-3-4) (elementary education)
• Organization
• National Level
: the Ministry of Education and HRD (the Ministry of Education) : Headed by Deputy Prime Minister : 540 employees
• Local Level
: Local Offices of Education (for elementary & secondary education) : Education Board : elected by members of each school council : Superintendent : serves four years
• Budget
• One year budget (2006) : 29.1 trillion Korean won
: 5.06% of the GNP : 19.7% of total Government budget
• the Biggest portion
: E lementary & Secondary Education 86.2% : Higher Education 12.3%
• Elementary Education Special School
• School System
Pre-school Education Secondary Education Miscellaneous School Civic High School Special Classes (Industrial Firms) Middle School Attached to Industrial Firms Middle School High School Trade High School Air&Correspondence High School High School Attached to Industrial Firms Higher Education College in the Company Cyber College and University Air&Correspondence University Junior College University of Education Graduate School Industrial University Technical College University Miscellaneous School Civic School Elementary School Kindergarten 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Schooling year Age
•
• Kindergarten
• Carried out in national, public and private
• 545,812 children in 8,290 kindergartens (2006)
• Government Project :
“ Supporting kindergarten tuition for children for low-income families ” Since 1999
• Aged 3 to 5
• Elementary Education
• Free and Compulsory
• Enrollment rate
• Early entering into Elementary school :
• 3,295,043 Children in 5,733 schools (2006)
64%(1945) 5 years old
• Teaching English in Elementary school since 1997
3rd grade of school        ► 98.8%(2005)
• Middle School
• 2,075,311 students in 2,999 middle schools (2006)
1985 :in farming and fishing areas
• 99% enrollment rate from elementary school
• Free compulsory middle school education
2002 :nationwide
• High School
• 1,775,857 students in 2,144 high schools (2006)
• 99.7% enrollment rate from middle school
• Kinds of high schools
General, Vocational, Science, Special high schools
• Curriculum & Textbooks
• 7th Curriculum (Current Curriculum)
devised by the Ministry of Education revised every 5 to 7 years providing basic education fostering initiative guaranteeing learner-centered education ensuring autonomy
• National Curriculum (by Education Law 155)
.
• Curriculum & Textbooks
• Textbook
1st Type : Copyrights are held by the Ministry of Education. 2nd Type : Published by private publishers & authorized by the Ministry of Education. 3rd Type : Recognized as relevant and useful by the Ministry, local education offices.
• Teachers and Teachers’ Organization
• Classification of Teachers
Teacher Grade l and ll ⓐ Assistant Teacher ⓑ Counselor ⓒ Librarian ⓓ Field Training Teacher ⓔ Nursing Teacher ⓕ
• Teachers and Teachers’ Organization
• Teachers’ Organization
T hree Teachers’ Unions : Korean Teachers & Educational Workers’ Union : Korea Union of Teaching & Educational Workers : Korea Liberal Teacher’s Union Teachers’ Association : Korean Federation of Teachers Association
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• Types of Higher Educational Institutions
• University
• Industrial University
• University of Education
• Junior College
• Air & Correspondence University
• Technical College
• College in the Company
• Cyber College & University
• Miscellaneous Schools
• Enrollment rate 82.1% (2006)
• Others
• Junior College
• 2 or 3 years
• 13 national/public, 139 private
817,994 students in 152 Junior colleges (2006) ■
• University
• 4 years
2,434,112 students
• 43 national/public, 178 private
• 6 year program : medicine, oriental medicine, dentistry
■ in 221 colleges and universities (2006)
• Cultivate high-quality research human resources by nurturing world class research universities and regional graduate schools of excellence
• Goal
• Brain Korea 21 Project
: efforts for improving the quality of higher education
• Outline
. 147,000 students, 569 project teams in 74 universities 79,680 students, 564 project teams in 166 universities 2.03 trillion KRW 1.4 trillion KRW 2006 ~ 2012 1999 ~ 2005 Phase Ⅱ Phase Ⅰ
• Brain Korea 21 Project
: efforts for improving the quality of higher education
• Visible result : Research atmosphere
. World Ranking The number of articles registered in SCI 18 3,765 Before BK21 10 (Goal) 12 7,281 (2005) Phase Ⅱ Phase Ⅰ
• Give a boost to regional economy by nurtur ing qualified human resources of regional university graduates through specialized education programs
• Goal
• NURI Project
: New University for Regional Innovation 1.2 trillion KRW is to be invested over a period of five years (2004~2008)
• Outline
131 project teams, 190,000 students 109 regional universities are currently participating as of 2006
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• Institutionalization of lifelong Education
• Air & Correspondence High school
• Air & Correspondence University
5,700 students in 11 schools(1974) Established in 1972
• Distance University Education
15 Universities & 2 Junior Colleges To working adolescents, laborers, housewives and etc. 273,417 students (2006) Opened March, 2001        ► 13,448 in 39 schools (2006) .
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• ICT Infrastructure in Schools and Classrooms
• All K-12 school classrooms connected to Internet in 2000.
• 1 PC per 5.6 students
• Educational Information Services
• EDUNET: National Teaching-Learning Center
: Comprehensive education information service launched in 1996
• Cyber Home Learning System & EBSi
: Internet-based learning service to complement school education at home : To enhance self-directed learning capabilities : 5.6 million members as of 2006 : 25.6 million materials are registered.
• Korea Education & Research Information Service, 1999
• National Education Information System (NEIS)
• National Education Information System
: launched in 2002 to improve efficiency and transparency
• Service areas
: General affairs – Accounting & HR managements : School affairs – Student record : connected with MOE&HRD, offices of education and all schools
• To contribute to narrowing down the digital divide in education
: Provision of refurbished PCs and teacher training
• e-Learning Globalization since 2005
• Seminars, joint research, and e-Learning consulting
(17 developing countries, 2006) : Civil service - Certifications
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• 3. Educational Policy Advisory Councils 2. National HRD Committee 1. Ministry of Education and HRD
• Elevated to the Ministry headed by a Deputy Prime Minster
• Oversee & coordinate policies
• Ministerial Education Policy Council
• Presidential Commission on Educational Innovation Council
• Composed of 14 line ministries (sub-cabinet meeting)
• Since 2001
• Monitor and evaluate the implementation of HRD policies
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• Reforming Educational System
• Change current subject-centered curriculum
to competency-based curriculum - Develop the Basic Competency Skills for All
• Enhance and evaluate teacher’s ability
- Promote autonomy and accountability in the school system
• Reforming Educational System
• Strengthen the support system to promote LLL
• Enhance quality of higher education up to global level
- Set up ubiquitous learning system (e-Learning) - Support LLL by financial aids (Loan, Tax Credits, ILA) - Innovate role and function of universities as new engine for sustainable economic growth - Expand cooperative system among industries, universities, and research institutes
• Thank You