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Educational Research & Statistics Research Application to Practice

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Title: Educational Research & Statistics Research Application to Practice


1
Educational Research StatisticsResearch
Application to Practice
  • Summer 2003
  • Dr. Chiang

2
Major Components of the Course
  • Understanding basic research principles and
    methods
  • Becoming familiar with educational statistics
  • Basic descriptive inferential statistics
  • Using SPSS to analyze data

3
Assignments for the Course
  • Completing the SPSS assignment
  • www.uwosh.edu/faculty_staff/chiang/

4
The Status Quo of Educational Research
  • Why do educators have so little regard for
    research?
  • I have no time for research.
  • research is not understandable irrelevant,
    inconclusive, contradictory
  • children are not guinea pigs
  • not in my job description
  • any other reasons?

5
If not research, what else drive our
decision-making?
  • political forces
  • federal, state, and local agencies
  • legislature and courts
  • teacher unions
  • Various advocacy groups
  • publishers
  • tests
  • textbooks

6
-- continued
  • fads or trends
  • media influence
  • teachers own or colleagues experience
  • common sense
  • any other factors?

7
Scientific Method
  • deductive reasoning (from general to specific)
    Aristotles syllogisms
  • inductive reasoning (from specific to general)
    F. Bacons field study
  • deductive-inductive C. Darwin

8
Similarities between educational and scientific
research
  • Describe
  • Explain
  • Predict
  • Control

9
Differences between educational and scientific
research
  • Complexity of subject matter
  • Observability of subject matter
  • Repeatability of subject matter

10
Four Scales of Measurement
  • Nominal (3 not gt 2)
  • Ordinal (3 gt 2, but 3-2 not 2-1)
  • Interval (3-2 2-1, but 100/2 not 50)
  • Ratio (100/2 50)

11
Basic vs. Applied Research
  • theory development
  • has long term value
  • draw conclusions
  • problem solving
  • socially important (immediately)
  • make recommendations

12
Descriptive Statistics
  • Measures of central tendencies
  • Mode
  • Median
  • Mean
  • Measures of variance
  • Range
  • Standard deviation

13
Modern Scientific Methods
  • Seven typical steps
  • identification of problem
  • definition of problem
  • formulation of hypothesis
  • development/selection of measure
  • collection of data
  • analysis of data
  • draw conclusions

14
Where can you locate topics for research?
  • any perplexing questions that you have
    encountered?
  • ask your colleagues for such questions
  • check over related journals/books/newspaper
    articles

15
Hypothesis
  • start with a research question
  • change the question into a null hypothesis
  • contrast null hypothesis with alternative
    hypothesis
  • compare a directional and a non-directional
    hypothesis
  • a good hypothesis is concise testable

16
Examples of Research Questions
  • Do women and men do equal amount of housework?
    (gender equity?)
  • Definition of housework
  • Data gathering procedure
  • Are black drivers more likely to be ticketed for
    speeding than white drivers? (racial profiling?)
  • Research method
  • Validity threats
  • Does hormone-replacement therapy (HRT) do more
    harm than good for women with PMS? (estrogen
    study, July 2002)
  • Sample
  • Statistics

17
Variables
  • Independent (grouping) variable (IV)
  • Dependent (test) variable (DV)
  • IV precedes DV
  • IV is to be experimentally manipulated
  • DV is to be measured
  • A hypothesis should contain only one IV and one
    DV

18
Review of literature
  • Start with the most recent research
  • Use Internet search engines
  • Use interviews for expert comments
  • Primary vs. secondary source
  • Research vs. discussion articles
  • Follow APA style
  • Paraphrase vs. quotation

19
Using t-tests to compare the means
  • Why is t-test considered an inferential
    statistics?
  • When do we use independent t?
  • To compare two groups that are mutually exclusive
    (e.g. experimental vs. control groups, males vs.
    females)
  • When do we use dependent (correlated) t?
  • To compare pretest vs. posttest (paired samples
    t)
  • To compare two groups that have been matched in
    pairs (e.g. studies of identical twins)

20
Sampling
  • purpose
  • random sampling
  • Each individual in the population has an equal
    independent chance to be selected.
  • stratified sampling
  • Either proportional or not proportional to
    population
  • systematic sampling
  • cluster sampling

21
When should we have larger N?
  • For studies of significant consequence
  • If the sample is very diversified
  • Minute differences are expected
  • For longitudinal studies
  • If you are to have subgroup analyses
  • Attrition of subjects are anticipated
  • Test measures are unreliable
  • Variables are complex and difficult to control

22
Survey research
  • Why is it lowly regarded?
  • How to improve return rate?
  • Use captured audience
  • Ensure confidentiality
  • Keep it short
  • Provide incentive
  • What is the purpose of a pilot study?

23
Observational research
  • Most useful in what situations?
  • Study early childhood or infants
  • Study social interactions
  • Study behavior changes
  • How to improve objectivity and reliability?
  • Define behaviors very clearly
  • Train observers
  • Report inter-rater reliability

24
Recording of observational data
  • event recording (frequency count)
  • duration recording
  • latency recording
  • interval recording
  • time sampling

25
Use correlation coefficient (r) to indicate
degree of relationship
  • Correlation vs. causation
  • Correlation coefficient ranges from 0 to 1 or 1
  • Scattergram is a graph showing the spread of data
    points (or line of best fit) between variables X
    Y.
  • Correlation matrix displays correlation
    coefficients among several variables
  • Interpretation of r
  • Pearson product-moment correlation (interval
    scale)
  • Spearmen rank-difference correlation (ordinal
    scale)

26
Ethics in Educational Research
  • Informed consent
  • Use of human subjects
  • Withdrawal from participation
  • Counterbalance treatments
  • Double blind procedures and use of placebo
  • Report not revealing recognizable identity
  • Citing sources to give credits

27
Use chi-square to analyze survey results
  • Chi-square is a non-parametric test (n gt 5 per
    cell)
  • The data are in frequency counts
  • Compare observed frequencies vs. expected
    frequencies
  • Arrange data in 2 X 2, 2 X 3 contingency tables
  • Use Crosstab function in the SPSS program to
    obtain crosstabulations and chi squares

28
Research Planning A Simulation
  • Why Johnny cant sleep?
  • Ferberizing a baby (vs. fertilizing the plots)?
  • Researchers bias
  • what variables have to be controlled?
  • What data to collect?
  • Did you agree on one study or have ideas for
    several studies?

29
Experiment vs. Investigation
  • What constitutes an experiment?
  • Manipulation of the independent variable
  • Its purpose is to demonstrate a functional or
    cause-and-effect relation between variables
  • Investigation involves analysis of data (without
    manipulation)
  • Ex post facto design
  • Cross sectional vs. longitudinal studies

30
Fundamental Principles of Experiment
  • Assume equivalence between the two groups, often
    by random assignment of subjects
  • Maintain treatment fidelity by following
    treatment scripts
  • Control extraneous variables
  • Have a reasonable experimental duration (e.g. a
    quarter)
  • Minimizes Pygmalion, Hawthorne, halo effects

31
Internal vs. External Validity
  • Which is absolutely essential? Why?
  • Relation between internal external validity is
  • Sequential (internal validity first, external
    next)
  • Reciprocal (too much internal validity will
    result in little or no external validity)

32
Threats to Internal Validity
  • history
  • maturation
  • testing
  • instrumentation
  • regression towards the mean
  • differential selection
  • experimental mortality or attrition
  • interactions of the above factors

33
Threats to External Validity
  • Reactive or interaction effects of pre-testing
  • Interaction effects of selection bias IV
  • Reactive effects of experimental arrangements
  • Multiple treatment interference

34
Research Design
  • Pre-experimental designs
  • One shot case study
  • One-group pretest-posttest design
  • Static group comparison
  • True experimental designs
  • Pretest-posttest control group design
  • Posttest only control group design
  • Solomon four group design
  • Quasi experimental design
  • Non-equivalent control group design

35
Single Subject Research
  • Compare single subject and group research
  • Reversal or ABAB design
  • Baseline pattern
  • Ethical concerns of reversal
  • Irreversible behaviors
  • Multiple baseline design
  • Across different behaviors
  • Across different subjects
  • Across different settings

36
Review
  • What does significant level of .05 .01 and .001
    mean?
  • Can you defend the use of null hypothesis?
  • Correlation is not causation. Give an example or
    two to explain why.
  • What is a scattergram or scatterplot for?
  • Explain what crosstabulation or crosstab is
    about?
  • How is bibliography different from reference?
  • Differentiate random sampling from random
    assignment.

37
Review 2
  • Give examples of dependent vs. independent
    variables
  • Explain internal and external validity.
  • Can single subject designs demonstrate functional
    relations?
  • Are survey studies typically experimental or
    investigation?
  • How is time sampling different from interval
    recording?
  • Is -.70 possible for an r? Is it also possible
    for a t test score? How about a chi-square test
    score?
  • Is degree of freedom related to homogeneity of
    subjects?

38
Review 3
  • Statistical concepts
  • Median versus mean
  • Inferential statistics
  • Critical value
  • Two-tailed test
  • Inverse correlation
  • Non-parametric test (continuous data vs.
    categories)
  • r and r square
  • Ordinal vs. Interval scale
  • Making conclusion statement after hypothesis
    testing
  • Hawthorne versus Pygmalion effect
  • Ex post fact research design
  • Cross-sectional versus longitudinal design
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