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EDLD 6392 Advanced Topics in Statistical Reasoning Texas A

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... of Variability (SD, variance, range) Causal-Comparative Research ... of Variance (MANOVA) ... 3-Score variances for the populations under study ... – PowerPoint PPT presentation

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Title: EDLD 6392 Advanced Topics in Statistical Reasoning Texas A


1
EDLD 6392Advanced Topics in Statistical
ReasoningTexas AM University-Kingsville
  • Research Designs and Statistical Procedures

2
Research Designs by Purpose
  • Educational Research is conducted for four
    primary purposes
  • 1-Description
  • 2-Prediction
  • 3-Improvement
  • 4-Explanation

3
Research Designs by Similarities
  • Experimental Quasi-experimental
  • -Involves Researcher Intervention
  • Non-experimental
  • - Examines phenomena as they exist
  • Descriptive, Causal-Comparative, and
    Correlational

4
Descriptive Research Designs
  • The Purpose
  • The description of natural or man-made
    phenomena-their form, actions, changes over time,
    and similarities-with other phenomena, an effort
    to describe. Involves making careful descriptions
    of educational phenomena, viewed as understanding
    what people or things mean.
  • Studies primarily concerned with determining
    what is.

5
Descriptive Research (Contd)
  • Types of Measurements
  • standardized achievement scores, classroom
    observation instruments, attitude scales,
    questionnaires, and interviews
  • Statistics
  • Central Tendency (mean, median, mode)
  • Measures of Variability (SD, variance, range)

6
Causal-Comparative Research
  • The Purpose
  • Purpose of explaining educational phenomena
    through the study of cause-and-effect
    relationships. The presumed cause is called the
    independent variable and the presumed effect is
    called the dependent variable. Designs where the
    researcher does not manipulate the independent
    variable are called ex post facto research.

7
Causal-Comparative (Contd)
  • Causal-Comparative research is also a type of
    non-experimental investigation in which
    researchers seek to identify cause-effect
    relationships by forming groups of individuals in
    whom the independent variable is present or
    absent and than determining whether the groups
    differ on the dependent variable.

8
Quasi-Experimental Research
  • Parametric Tests
  • Statistical Analysis The t Test
  • For testing the significance of difference
    between two sample means
  • Basic Assumptions
  • 1-Scores form an interval or ratio scale
  • 2-Scores are normally distributed
  • 3-Score variances for the populations under
    study are equal (SDSD)

9
Quasi-Experimental (Contd)
  • Analysis of Variance (ANOVA)
  • Comparison of two or more group means
  • Multivariate Analysis of Variance (MANOVA)
  • Statistical technique for determining whether
    groups differ on more than one dependent
    variable.
  • Basic Assumptions
  • 1-Scores form an interval or ratio scale
  • 2-Scores are normally distributed
  • 3-Score variances for the populations under
    study are equal (SDSD)

10
Quasi-Experimental (Contd)
  • Nonparametric Tests
  • Nonparametric statistics tests statistical
    significance that do not rely on any assumptions
    about shape or variance of population scores.
  • Used with measures that yield categorical or
    rank scores, or do not have equal intervals.
    Nonparametric tests are less powerful, they
    require larger samples to yield the same level
    statistical significance.
  • 1-The Chi-Square Test used to determine
    whether research data in the form of frequency
    counts are distributed differently for different
    samples.

11
Quasi-Experimental (Contd)
  • Nonparametric Tests (Contd)
  • 2-The Mann-Whitney U testused to determine
    whether the distributions of scores of two
    independent samples differ significantly from
    each other.
  • 3-The Wilcox signed rank testused to determine
    whether the distributions of scores of two
    samples differ significantly from each other when
    the scores of the samples are correlated.

12
Quasi-Experimental (Contd)
  • Nonparametric Tests (Contd)
  • 4-The Kruskal-Wallis testIf more than two
    groups of subjects are to be compared, a
    nonparametric one-way analysis of variance
    (Kruskal-Wallis) can be used.

13
Classification of Research Design
(Causal-Comparative)
X
O1
O2
One-group pretest-posttest design
Group 1
O1
X
O2
Nonequivalent control group
Group 2
O3
O4
O1
X1
X2
O2
Equivalent time-samples design
14
Non-experimental ResearchCorrelational Designs
  • The Purpose
  • To discover relationships between variables
    through the use of correlational statistics.
    Involves correlating data on two or more
    variables for each individual in a sample and
    computing a correlation coefficient.
  • Two major purposes
  • 1-To explore causal relationships between
    variables
  • 2-To predict scores on one variable from
    research participants scores on other variables.

15
Correlation Research Design
  • Advantages
  • 1-Enables researchers to analyze the
    relationships among a large number of variables
    in a single study.
  • 2-They provide information concerning the degree
    of the relationship between the variables being
    studied.
  • Parametric Test
  • Pearson r statistical procedure
  • Basic Assumptions
  • 1-Scores form an interval or ratio scale
  • 2-Scores are normally distributed
  • 3-Score variances for the populations under
    study are equal (SDSD)

16
Scattergrams Representing Different Degrees and
Directions of Correlation between Two Variables
Positive correlation (r.99)
Negative correlation (r-.73)
Grade point
I.Q.
Age
gpa
Computer use
17
Choosing Statistical Procedures
START
Interval Data
Relate
Compare
Normal
Not Normal
SD
SD
Spearman Correlation
Pearson Correlation
Dependent
Independent
2 groups
gt2 groups
2 groups
gt2 groups
Mann-Whitney
Wilcoxon
Friedman ANOVA
Kruskal-Wallis
Independent
Dependent
gt2 groups
2 groups
2 groups
gt2 groups
Independent Samples t Test
ANOVA
Related Samples t-Test
Repeated Measures ANOVA
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