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5. A reflective researcher with skills in:
(a) Reading and evaluating research

5a2 Annotated bibliography

Development Plan Portfolio Documentation
Read selected books, journals and online resources. Write an annotated bibliography on qualitative research.
  1. Notes
    1. Evaluating Research in Academic Journals
    2. Research on Educational Innovations
    3. Understanding Research Methods
  2. List of References
  3. Bibliography

1. Notes

1.1 Evaluating Research in Academic Journals, Frederick Pyrczak

Pyrczak is written from the perspective of the evaluator who is critically reading the research, but it is an excellent checklist to use when performing or reporting research. The introduction sets our anumber of guidelines, then the rest of the book segments the research process into logical steps or phases, and poses a set of insightful questions for the would-be researcher to answer for each phase.
1.
Introduction to Evaluating Research Reports
2.
Evaluating Titles
3.
Evaluating Abstracts
4.
Evaluating Introductiions and Literature Reviews
5.
A Closer Look at Evaluating Literature Reviews
6.
Evaluating Samples When Researchers Generalize
7.
Evaluating Samples When Researchers Do Not Generalize
8.
Evaluating Instrumentation
9.
Evaluating Experimental Procedures
10.
Evaluating Results Sections
11.
Evaluating Discussion Sections
12.
Putting It All Together

1.2 Research on Educational Innovations, Arthur K. Ellis and Jeffrey T. Fouts

While I found this book to be interesting and quite readable, its target audience is educators, and much of the content is specific to that profession.  However, some of the chapters can readily be applied in a more general setting.  For instance:

5.
7.
11.

Innovations from Brain Research
Thinking Skills Programs
Cooperative Learning

I was interested in the chapter on Cooperative Learning because of a valuable course I took from Dr. Larry Burton (EDCI665 Advanced Instructional Models: Cooperative Learning for Adults)

The whole area of brain research and its relation to learning is a fascinating one, and while it has critical implications for teaching children whose brains are still developing physiologically, leaning is a life-long experience, and the same principles set out in Caine and Caine's brain-based learning principles and teaching model are always relevant. (page 59)

Principle One: The Brain Is a Parallel Processor.
Principle Two: Learning Engages the Entire Physiology.
Principle Three: The Search for Meaning Is Innate.
Principle Four: The Search for Meaning Occurs Through "Patterning".
Principle Five: Emotions Are Critical to Patterning.
Principle Six: Every Brain Simultaneously Perceives and Creates Parts and Wholes.
Principle Seven: Learning Always Involves Both Focused Attention and Peripheral Perception.
Principle Eight: Learning Always Involves Conscious and Unconscious.
Principle Nine: We Have Two Types of Memory: A Spatial Memory System and a Set of Systems for Rote Learning.
Principle Ten: The Brain Understands and Remembers Best When Facts and Skills are Embedded in Natural Spatial Memory.
Principle Eleven: Learning is Enhanced by Challenge and Inhibited by Threat.
Principle Twelve: Each Brain is Unique.

The notion of "hemisphericity" continues to be supported by research, although there does not yet seem to any proven application of the theory. (page 62)

   "Sperry's research supports the idea that the two hemispheres of the brain serve differing but complementary functions.  A person uses both hemispheres when learning or functioning, but one may dominate the other and determine a person's style or preferred way of learning.  Each hemisphere is thought to contribute specialized functions to tasks.  The left hemisphere of the brain is associated with verbal, sequential, analytical abilities.  The right hemisphere is associated with global, holistic, visual-spatial abilities.  Two related ideas are full lateralization and parallel processing.  In lateralization, the left hemisphere dominates in language expression while the right hemisphere dominates in nonverbal processing.  In parallel processing, research indicates that the brain hemispheres perform many tasks simultaneously.

   "The concept of different functions for the two hemispheres of the brain seems now to be widely accepted, with the left brain controlling linear activity and the right brain controlling global activity.  Programs have emerged to teach to both sides of the brain or to compensate for a weaker hemisphere.  However, this conclusion is questioned by a number of researchers and psychologists.  For example, Zale, Sink, and Yachimowicz (1992) concluded that 'there is little empirical support to compensate for hemisphericity through teaching integrative process techniques,' and that 'the notion of cerebral dominance has limited theoretical or practical value for educators. . .'"

 
In 1995, Daniel Goleman became famous "for his construct of emotional intelligence".  Goleman says: "In a very real sense we have two minds, one that thinks and one that feels." (page 63)  I attended a seminar conducted by Dr. Robert Cooper on the topic of emotional intelligence in which he spoke of the importance in the business world of acknowledging the power of emotions in working relationships on the job. He has written a book on the subject called "Executive EQ: Emotional Intelligence in Leadership and Organizations".

In their list of ideas emerging from brain-based research (page 64), Ellis and Fouts report some findings that link learning and emotions:

In chapter 7, Thinking Skills Programs, the authors presented a very useful "A Taxonomy of Thinking Skills" adapted from B. K. Beyer (1988). (page 93)

I. Thinking Strategies
Problem Solving
1.
Recognize a problem
2.
Represent the problem
3.
Devise / choose solution plan
4.
Execute the plan
5.
Evaluate the solution
Decision Making
1.
Define the goal
2.
Identify alternatives
.3.
Analyze alternatives
4.
Rank alternatives
5.
Judge highest-ranked alternatives
6.
Choose "best" alternative
Conceptualizing
1.
Identify examples
2.
Identify common attributes
3.
Classify attributes
4.
Interrelate categories of attributes
5.
Identify additional examples / non-examples
6.
Modify concept attributes / structure
II. Critical Thinking Skills
1.
Distinguishing between verifiable facts and value claims
2.
Distinguishing relevant from irrelevant information, claims or reasons
3.
Determining the factual accuracy of a statement
4.
Determining the credibility of a source
5.
Identifying ambiguous claims or arguments
6.
Identifying unstated assumptions
7.
Detecting bias
8.
Identifying logical fallacies
9.
Recognizing logical inconsistencies in a line of reasoning
10.
Determining the strength of an argument or a claim
III. Information-Processing Skills
1.
Recall
2.
Translation
3.
Interpretation
4.
Extrapolation
5.
Application
6.
Analysis (compare, contrast, classify, seriate, etc.)
7.
Synthesis
8.
Evaluation
9.
Reasoning (inferencing): inductive, deductive, analogical

Source: Adapted from Beyer, B. K. (1998a). Developing a scope and sequence for thinking skills instruction.  Educational Leadership, 45(7),27.

1.3 Understanding Research Methods, Mildred L. Patten

This book is valuable in the sense of being a greatly expanded glossary.  All the terms that are used in research are introduced in a meaningful context that helps define them.  I have created a index of the terms for my own reference. The book is organized into mostly one-page topics, making it unnecessary to use page numbers to locate the material.

A. Introduction to Research Methods
Topic Key Words
1. Introduction to Empirical Research
  1. empirical
  2. observation (rather than theory)
  3. hypothesis
  4. experimental
  5. nonexperimental
2. Experimental vs. Nonexperimental Studies
  1. experiments
  2. experimental group
  3. control group
  4. nonexperimental studies
3. Experimental vs. Causal-Comparative Studies
  1. cause-and-effect relationships
  2. causal-comparative studies
4. Types of Nonexperimental Research
  1. causal-comparative research
  2. survey
  3. census
  4. Case study
  5. field research
  6. longitudinal research
  7. correlation research
  8. historical research
5. Variables in Nonexperimental Studies
  1. variable
  2. mutually categories
  3. exhaustive categories
  4. independent variables
  5. dependent variable(s)
6. Variables in Experimental Studies
  1. independent variable
  2. dependent variable(s)
  3. physically manipulate
7. Research Hypotheses, Purposes, and Questions
  1. research hypothesis
  2. directional hypothesis
  3. nondirectional hypothesis
  4. research purpose
  5. research question
  6. null hypothesis
8. Operational Definitions of Variables
  1. conceptual definitions
  2. operational definition
9. Quantitative vs. Qualitative Research: I
  1. quantitative research
  2. qualitative research
  3. deductive approach
  4. inductive approach
  5. generalize
10. Quantitative vs. Qualitative Research: II
  1. inherent leaning
  2. little is known -> qualitative
  3. subjects closed or secret culture -> qualitative
  4. subjects not available for extensive interaction -> quantitative
  5. limited funds -> quantitative
  6. 'hard numbers' -> quantitative
  7. blend
11. Program Evaluations
  1. program evaluation / evaluation research
  2. experimental research
  3. applied research
  4. needs assessment
  5. formative evaluation
  6. summative evaluation
12. Ethical Considerations in Research
  1. protect subjects from both physical and psychological harm
  2. right to know the purpose of the research
  3. informed consent
  4. debriefing
13. The Role of Theory in Research
  1. reinforcement theory
  2. deduce hypotheses
  3. induce theory
  4. grounded theory
  5. methodology
  6. trends across groups
B. Reviewing Literature
Topic Key Words
14. Reasons for Reviewing Literature
  1. replication study
  2. strict replication
  3. modified replication
  4. identify testable hypotheses
  5. avoid dead ends
  6. identify measuring tools - instruments
  7. locate, use, and cite relevant research
15. Locating Literature Electronically
  1. sources, databases, depending on research area
  2. keyword and text searches
  3. Appendix C
16. Writing Literature Reviews
  1. name and describe the broad problem area
  2. establish importance
  3. topic by topic description of relevant research
  4. include results of research cited
  5. summarize if literature review is long
C. Sampling
Topic Key Words
17. Biased and Unbiased Sampling
  1. sample
  2. population
  3. census
  4. unbiased sample
  5. simple random sample
  6. samples of convenience (accidental samples)
  7. volunteerism
  8. inferring from a sample to a population - generalizing
18. Simple Random and Systematic Sampling
  1. simple random sample
  2. sampling error
  3. systematic sampling
19. Stratified Random Sampling
  1. sampling error - precision
  2. stratified random sample
  3. stratification helps only we stratify the variable that is relevant
20. Other Methods of Sampling
  1. cluster sampling (clusters drawn at random)
  2. purposive sampling - purposively select those whom we believe will give us the best information as participants
  3. snowball sampling
  4. multistage sampling
21. Introduction to Sample Size
  1. increasing sample size increases precision, but typically does not correct for bias
22. A Closer Look at Sample Size How many participants do I need?
  1. pilot studies
  2. expensive, time consuming
  3. expected rate of occurrence of phenomenon we wish to study
  4. variability in our population
  5. size of effect/difference - small -> large sample
D. Measurement
Topic Key Words
23. Introduction to Validity
  1. instrument (measuring tool)
  2. valid when it measures what is is supposed to measure
  3. partial validity
  4. relative validity
  5. validity is a matter of degree
24. Judgmental Validity
  1. content validity
  2. make judgments about the content validity
  3. face validity - valid on the face of it
25. Empirical Validity
  1. planned comparisons, related to criteria - ratings or standards
  2. predictive validity
  3. ranked ratings
  4. validity coefficients - correlation coefficient
  5. criterion-related validity
26. Judgmental-Empirical Validity
  1. construct validity - relies heavily on subjective judgments and empirical data (ie, based on observations)
  2. construct - collection of related behaviors or indicators
27. Reliability and its Relationship to Validity
  1. reliability = consistent results
  2. high reliability does not imply high validity
Reliable and valid
Reliable but invalid.  Not useful.
Unreliable, undermines valid aim.  Not useful.
Unreliable and invalid.  Not useful.
28. Measures of Reliability
  1. classic approach - do duplicate measurements
  2. interobserver reliability
  3. test-retest reliability
  4. parallel-forms reliability
29. Norm- and Criterion-Referenced Tests
  1. norm-referenced tests - NRT
  2. criterion-referenced tests - CRT
  3. standardized - test has standard directions for administration and interpretations
30. Measures of Optimum Performance
  1. achievement test - measures knowledge and skills people have acquired
  2. aptitude test - predicts some specific kind of achievement
  3. intelligence test - predicts achievement in general
31. Measures of Typical Performance The issue is how to reduce the effect of social desirability of subjects' responses.
  1. preserve anonymity
  2. make observations without subjects' awareness, where ethically possible
  3. use projective techniques (like ink blots)
E. Experimental Design
Topic Key Words
32. True Experimental Designs The following are called true designs because of strong internal validity:
  1. pretest-posttest randomized control group design
  2. posttest only randomized control group design
  3. Solomon randomized four-group design
33. Threats to Internal Validity
  1. history - other environmental influences during the testing period
  2. maturation - people making the observations have improved in doing it
  3. testing - the effects of the pretest on the posttest
  4. statistical regression - from selecting subjects based on their extreme scores
  5. selection - when assignment is not random
  6. selection threat can interact with all the other threats - eg, selection-history interaction
  7. selection can also interact with the mortality threat
  8. all threats to internal validity can be overcome by using a true experimental design
34. Threats to External Validity
  1. selection bias
  2. reactive effects of experimental arrangements
  3. reactive effect of testing (also called pretest sensitization)
  4. multiple-treatment interference
  5. distinction between internal and external validity
    1. external - to whom and under what circumstances can the results be generalized?
    2. internal - is the treatment, in this particular case, responsible for the observed changes(s)?
35. Pre-Experimental Designs
  1. these are of little value for investigating cause-and-effect because of poor internal validity
  2. one-group pretest-posttest design
  3. one-shot case study
  4. static-group comparison design
36. Quasi-Experimental Designs
  1. these are of intermediate value for exploring causal relationships
  2. nonequivalent control group design
  3. equivalent time-samples design
F. Understanding Statistics
Topic Key Words
37. Descriptive and Inferential Statistics
  1. descriptive statistics - summarize data
  2. inferential statistics - inferences about the effects of sampling errors
  3. significance tests are an important family of inferential statistics
  4. parameters describe values from a census, statistics describe values from studies in which samples were examined
    • Samples yield Statistics
    • Populations yield Parameters
38. Introduction to the Null Hypothesis
  1. The observed difference was created by sampling error (random errors, not those created by bias).
  2. There is no true difference between the groups.
  3. The true difference between the groups is zero.
  1. Significance tests determine the  probability that the null hypothesis is true.
  2. The null hypothesis would be rejected if there was a statistically significant difference.
39. Scales of Measurement No Oil In Rivers
  1. Nominal
  2. Ordinal
  3. Interval
  4. Ratio
40. Descriptions of Nominal Data
  1. classify subjects according to names (words) instead of numbers
  2. frequencies
  3. numbers of cases
  4. percentages
  5. proportions
41. Introduction to the Chi Square Test
  1. c2 chi square - usual test of the null hypothesis
42. A Closer Look at the Chi Square Test
  1. use of chi square test in a bivariate analysis
  2. Type I Error: Rejecting the null hypothesis when it is, in fact, a correct hypothesis.
  3. Type II Error: Failing to reject the null hypothesis when it is, in fact, an incorrect hypothesis.
  4. Minimize the probability of a Type I error by using a low probability such as 0.05 or less.
43. Shapes of Distributions
  1. quantitative data can be presented as a frequency distribution
  2. frequency polygon
  3. normal distribution
  4. skewed - left is positive, right is negative
44. The Mean, Median, and Mode
  1. mean = value around which yhr deviations sum to zero; sum of values divided by number of values
  2. it is drawn in the direction of extreme scores
  3. median = the middle score; it is used when a distribution is highly skewed
  4. mode = most frequently occurring score
45. The Mean and Standard Deviation
  1. a normal distribution can be described by its mean and standard deviation
  2. 68% of the cases lie within one standard deviation of the mean, in a normal distribution
46. The Median and the Interquartile Range
  1. median is used when the distribution is highly skewed
  2. when reported, it is customary to report range or interquartile range as a measure of variability
47. The Pearson Correlation Coefficient
  1. correlation coefficient is an indicator of the relationship between two quantitative sets of scores
  2. most widely used coefficient is the Peason product-moment correlation coefficient, or Pearson's r
  3. direct relationship (positive)
  4. inverse relationship (negative)
48. The t Test
  1. the t test is often used to test the null hypothesis regarding the observed difference between two means
  2. when the difference is statistically significant (p < .05), we reject the null hypothesis
  3. Causes of low probability in the t test:
    1. sample size
    2. size of difference in means
    3. amount of variation in the population
49. One-Way Analysis of Variance
  1. ANOVA (Analysis of Variance) is an alternative test for the observed difference in two means.
  2. Instead of t, it yields a statistic called F as well as degrees of freedom df,
  3. A single t test compares only 2 means, but a single ANOVA can compare several means.
  4. The null hypothesis says that the entire set of differences was created by sampling error.
  5. If the differences are significant, ANOVA does not tell you which ones. You need multiple comparisons tests to do that.
  6. This is called single-factor ANOVA because we calssify the subjects in only one way.
50. Two-Way Analysis of Variance
  1. Answers questions about the main effect, and about interaction.

2. List of References

Camilli, Thomas. (1992). A Case Of Red Herrings: Solving mysteries through critical questioning. (EN-0029)
Critical Thinking Books & Software. ISBN: 089455462X.

Cooper, Robert. (1997). Executive EQ; Emotional Intelligence in Leadership and Organizations. (EN-0157)
Grosset/Putnam. ISBN: 0399142940

Eisner, E. W. (1998). The Enlightened Eye: Qualitative Inquiry and the Enhancement of Educational Practice. (EN-0032)
Columbus, OH: Merrill Publishers. ISBN: 135314194

Ellis, Arthur & Fouts, Jeffrey. (1997). Research on Educational Innovations. (EN-0042)
Second Edition. Princeton Junction, NJ. Eye on Education Publishers. ISBN: 1-883001-05-6.

Gray, Paul and Watson, Hugh J. (1998). Decision Support in the Data Warehouse. (EN-0013)
Upper Saddle River, NJ: Prentice Hall. ISBN: 0137960794
Retrieved 20-Jun- 2004
URL: http://www.amazon.com/exec/obidos/ASIN/0137960794/qid=935963458/sr=1-2/002-6604344-2126820 (editorial reviews)

Haley, Barbara Jean. (1997b). Implementing the Decision Support Infrastructure: Key Success Factors in Data Warehousing (Information Technology). (EN-0066)
Unpublished Ph.D., University of Georgia.

Heise, David. (1997). Data Warehousing. M Comp. (EN-0648).

Hirji, Karim Khan. (1996). Information Processing, Enabling Technology and Coordination Modeling in Complex Systems: the Case of Data Warehousing. (EN-0107)
Unpublished Masters.

Inmon, William H. and Hackathorn, Richard D. (1994). Using the data warehouse. (EN-0014)
New York: Wiley. ISBN: 0471059668 (acid-free paper)

John, George Harrison. (1997). Enhancements to the Data Mining Process (Machine Learning, Pattern Recognition, Stock Selection). (EN-0106)
Unpublished Ph.D.

Kim, Hyeoncheol. (1998). Knowledge Discovery: a Neural Network Approach (Rule Extraction). (EN-0119)
Unpublished Ph.D.

Little, Robert Grover Jr. (1998). Identification of Factors Affecting the Implementation of Data Warehousing (Firms, Decision-Making). (EN-0100)
Unpublished Ph.D.

Lyne, Lawrence S. (1999). A Cross Section of Educational Research. (EN-0192)
Los Angeles, CA. Pyrzcak Publishing. ISBN: 1-884585-16-7.

Merriam, Sharon. B. (1998). Case Study Research in Education: A Qualitative Approach. (EN-0321)
San Francisco, CA: Jossey-Bass Publishers. ISBN: 787910090.

Park, Yong-Tae. (1999). The Effects of Data Warehousing (DW) As a DSS Database Component On Decision Performance: an Experimental Study of DW in DSS Contexts (Decision Support Systems). (EN-0093)
Unpublished Ph.D.

Patten, Mildred L. (2000). Understanding Research Methods. (EN-0191)
Los Angeles, CA. Pyrzcak Publishing. ISBN: 1-884585-22-1.

Pyrczak, Fred. (1999). Evaluating Research in Academic Journals. (EN-0190)
Los Angeles, CA. Pyrzcak Publishing. ISBN: 1-884585-19-1.

Research Methods Knowledge Base (EN-0765)
Retrieved 20-Jun- 2004
URL: http://trochim.human.cornell.edu/kb/index.htm

Sriram, Cadambi. (1997). Using Materialized Views in Data Warehouses. (EN-0017)
Unpublished M.Sc., Queen'S University At Kingston (Canada).

Wolcott, Harry F. (1990). Writing Up Qualitative Research. (EN-0037)
Newbury Park, CA. SAGE Publications Inc. ISBN: 0-8039-3792-X.

3. Bibliography

Other Recommended Reading

Bracey, Gerald W. (1997). Setting The Record Straight: Responses to Misconceptions About Public Education in the United States. (EN-0315)
Alexandria, VA: Association for Supervision and Curriculum Development (ASCD). ISBN: 0-87120-279-4

Coffey, Amanda & Atkinson, Paul. (1996). Making Sense of Qualitative Data. (EN-0316)
Thousand Oaks, CA: Sage Publications.

DuBrin, Andrew J. (1995). Leadership: Research Findings, Practice, and Skills. (EN-0317)
Boston: Houghton Mifflin. ISBN: 0-395-65634-6.

Gramme, Steven Joseph. (1996). An Intelligent EIS With a Data Warehouse Facility. (EN-0112)
Unpublished Masters.

Hacket Group. (1998). Study: Information Hard to Come By. (EN-0079)
Computerworld, 32(51), 64.

Hanson, Joseph H. (1996). An Alternative Data Warehouse Structure For Performing Updates (Database, SDB). (EN-0108)
Unpublished Ph.D.

He, Jianhui. (1999). A Case Study On an Implementation of Marketing Data Analysis System. (EN-0096)
Unpublished Masters.

Holstein, James A. & Gubrium, Jaber F. (1995). The Active Interview. (EN-0320)
Thousand Oaks, CA: Sage Publications.

Huyn, Nam Quan. (1997). Maintaining Data Warehouses Under Limited Source Access (View Maintanance). (EN-0103)
Unpublished Ph.D.

Inmon, William H., Imhoff, Claudia and Sousa, Ryan. (1998). Corporate information factory. (EN-0062)
New York: Wiley. ISBN: 0471197335 (cloth alk. paper)

Klein, Michel and Methlie, Leif B. (1995). Knowledge-based decision support systems : with applications in business (2nd ed.). (EN-0015)
Chichester, England ; New York: Wiley. ISBN: 0471952958

Kuno, Harumi Anne. (1996). View Materialization Issues in Object-Oriented Databases. (EN-0114)
Unpublished Ph.D.

Kwon, Sunhee. (1999). Clustering in Multivariate Data: Visualization, Case and Variable Reduction. (EN-0115)
Unpublished Ph.D.

Lewis, Bob. (1998). Helping the business is more important than managing information. (EN-0085)
InfoWorld, 20(37), 93.

McGee, Kimberlee Roi. (1997). The Bottlenecks of Implementing a Successful Data Warehouse. (EN-0104)
Unpublished Masters.

Millman, Howard. (1998). Leveraging Web portals. (EN-0087)
InfoWorld, 20(52/01), 43.

Mott, Randy. (1998). Knowledge payback. (EN-0088)
InformationWeek(700), p298 291p.

Nakabo-Ssewanyana, Sarah. (1999). Statistical data: The underestimated tool for higher education management. (EN-0072)
Higher Education, 37(3), 259.

Patterson, James Elder. (1996). Workflow and Data Warehousing Taxonomy. (EN-0109)
Unpublished Masters.

Poe, Vidette, Klauer, Patricia and Brobst, Stephen. (1998). Building a data warehouse for decision support (2nd ed.). (EN-0020)
Upper Saddle River, NJ: Prentice Hall PTR. ISBN: 0137696396

Poe, Vidette and Reeves, Laura L. (1997). Building a data warehouse for decision support. (EN-0012)
Upper Saddle River, NJ: Prentice Hall PTR. ISBN: 0135906628

Quass, Dallan Wendell. (1997). Materialized Views in Data Warehouses (Query Processing). (EN-0102)
Unpublished Ph.D.

Rallapalli, Prasad Venkataramana. (1997). View-Less Value Based Security (Database, Query Modification, Dynamic Views, SQL). (EN-0105)
Unpublished Ph.D.

Roberts, Elizabeth S. (1999). In defence of the survey method: An illustration from a study of user information satisfaction. (EN-0074)
Accounting & Finance, 39(1), 53.

Sabherwal, Rajiv. (1999). The relationship between information system planning sophistication and information system. (EN-0083)
Decision Sciences, 30(1), 137.

Shaposhnikov, Artyom. (1998). Algorithms For Efficient Transaction Management and Consistent Queries in Client-Server Semantic Object-Oriented Parallel Databases (Lazy Queries). (EN-0120)
Unpublished Ph.D.

Shim, Junho. (1998). An Intelligent Cache Manager in Data Warehousing Environment and Its Application to the Web Caching (World Wide Web). (EN-0099)
Unpublished Ph.D.

Simmons, Wayne A. (1998). Decision Support Is Driving Asset Mgm't. (EN-0086)
Internetweek, 738(p43), 1/3p.

Smith, Evelyn Webb. (1998). The Impact of Educational Technology On Media Center Programs in Dekalb County (Georgia). (EN-0098)
Unpublished Ph.D.

Stefanovic, Nebojsa. (1997). Design and Implementation of On-Line Analytical Processing (OLAP) of Spatial Data. (EN-0101)
Unpublished Masters.

Stohr, Edward A.; Konsynski, Benn R. (1992). Information systems and decision processes. (EN-0054)
Los Alamitos, Calif.: IEEE Computer Society Press. ISBN: 0-8186-2802-2 (casebound); 0-8186-2801-4 (microfiche)

Straughan, Roger. (1988). Can We Teach Children to Be Good: Basic Issues in Moral, Personal, and Social Education. (EN-0322)
Bristol, PA: Open University Press. ISBN: 0-335-09525-9 (hardback); 0-335-09524-0 (paperback)

Tam, Yin Jenny. (1998). Datacube: Its Implementation and Application in Olap Mining. (EN-0092)
Unpublished Masters.

Thierauf, Robert J. (1999). Knowledge management systems for business. (EN-0064)
Westport, Conn.: Quorum. ISBN: 1567202187 (alk. paper)

Thong, James Y. L. (1999). An Integrated Model of Information Systems Adoption in Small Businesses. (EN-0078)
Journal of Management Information Systems, 15(4), 187.

Vandenbosch, Betty. (1999). An empirical analysis of the association between the use of executive support systems and Accounting Organizations & Society, 24(1), 77. (EN-0082)

Velayas, James Michael. (1992). Strategic Management Decision Support For A Firm In Pursuit Of The Displaced Ideal Utilizing Data Envelopment Analysis And Entropy. (EN-0137)
Unpublished Ph.D.

Wang, Rihui. (1997). On the Design of a Secure Data Warehouse. (EN-0094)
Unpublished Masters.

Watson, Hugh J. and Haley, Barbara J. (1999). Managerial considerations. (EN-0090)
Communications of the ACM(Vol. 41 Issue 9), 32.

White, Gary Leon. (1996). Data Integration With Data Warehousing and Data Mining in Database Environments. (EN-0111)
Unpublished Masters.

Wiersma, W. (1999). Research methods in education: An introduction (7th ed.). (EN-0825)
Boston: Allyn & Bacon.

Witherell, Carol & Noddings, Nel, (Eds). (1991). Stories Lives Tell: Narrative and Dialogue in Education. (EN-0323)
New York, NY: Teachers College Press.

Zhuge, Y. U. E. (1999). Incremental Maintenance of Consistent Data Warehouses (Whips, View Maintenance). (EN-0097)
Unpublished Ph.D.


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