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Conducting Educational Research: Ch. 9- Analyzing and Interpreting Experimental Research
EDCI 696Dr. D. BrownPresented by: Kim Bassa
Targeted TopicsAnalysis of dependent variables and different types of dataSelecting the appropriate statistic for experimental design and data Interpreting experimental analysisDisplaying experimental design dataDiscussing experimental results
The “Best” FitIt is important to choose the best design to…
Fit your research questionAddress validity issuesSelect ways to measure your dependent variable
Experimental Research
Experiments that have treatments but do not use random assignment to make comparisons are called
quasi-experiments.
Experimental research and Quasi-experimental are alike in that they attempt to determine if an independent variable had a
direct impact on a dependent variable.
Throughout the text they are both referred to as experimental research
Experimental DataData from experimental research are quantitative or qualitative.You will collect numerical data on your variables in order to conduct your analysis. You will use inferential statistics to make comparisons between conditions in an experimental intervention or across/between experimental groups.
Types of Data:
• Ordinal• Nominal• Interval• Ratio (Scale)
Statistic Selection
Statistical Tests rely on certain assumptions in order to provide accurate information.When assumptions for a statistical test are violated (or not tested), the results of the analyses will be invalid. In each experiment prior to the analysis, the researcher will set the level of significance, which is the probability.
Statistic Selection
Level of Significance is
represented by the small letter…
• p
Level of Significance is also
represented by the Greek letter
for…
• alpha
5% probability statistical differences
in an analysis would be due to chance or
measurement error is represented by…
• p<.05
Level of Significance (probability)
P<.05 means there is 95% statistical probability that differences are due to the intervention.
Statistic SelectionSelecting or matching the right statistic depends on the
type of data you and whether you want to compare mean scores for the dependent variable across groups or
conditions.
Statistic SelectionPutting It All Together
(Table 9.2)
Can students find main idea after learning a new
reading strategy?
Design: Post –onlyData: correct/incorrect or percentage correct (nominal or interval)
Statistic: descriptives
Do Students in Algebra I classes who engage in the XYZ curriculum perform significantly different on the state tests than students who
do not?
Design: Comparison group
Data: correct/incorrect or percentage correct (nominal or interval)
Statistic: chi-square or ANOVA
Statistic Selection
To compare pre/post nominal data
Use chi-square
To compare percentage c
orrect interval data
Use ANOVA
The t test only test means between 2 groups
The ANOVA tests scores for multiplegroups at a time.
Statistic SelectionTwo-way ANOVA: two independent variables with one rating to analyze
ANCOVA: another variable accounts
for difference in or covary with the
dependent variableMANCOVA:
several ratings to analyze as separate
dependent variables or any
suspected covariance
Main Effects: yielded results after analyzing multiple
variables, independent or
dependent
Interaction Effects: the interaction of the
effects of two or more independent
variables on a dependent variable
Post hoc: follow ups to the original
statistical test (i.e. Bonforonni or
Tukey)
Interpretation of ResultsData Entry
Software: Statistical Package for the Social Sciences (SPSS) or Statistical Analysis Software (SAS)Use a new computer file Enter data collection accuratelyKnow how to use software menus and commands
Understanding OutputKnow the statistic for the test that you run (i.e. F statistic for ANOVA )Know the level of significance (i.e. p<.05)Know the degrees of freedom (df)- approximately equal the number of participants for your data and used in the statistical calculation of the level of significance
Interpretation of Results
Reporting Experimental Results
The results section is where statistical out-comes are reported and only
includes factual information from the data-base outcomes.
The effect size (degree of difference between groups or conditions) is also reported in
the results section.
Interpretation of ResultsData displays provide the advantage of visually analyzing data. frequency tables and histograms are two useful displays and are commonly use to report data.
A frequency table is used to display nominal or categorical data.
A histogram display the relationship between two variables whose measures yield continuous scores.
Discussing Experimental ResultsThe researcher has the opportunity to interpret the results in the discussion section.This section should also include limitations, implications for practice, and future research needs.
Limitations
•Shortcomings
Implications for Practice
•How results can be used in the classroom or other use
Future Research Needs
•New/Improved ideas for research or an extension to the current research
Statistical Conclusion Validity
Statistical Conclusion Validity: is based on reliable implementation of independent variable as well as appropriate and correctly used measures and statistics, in order draw conclusions regarding the effect of the independent variable on the dependent variable.
Fidelity of Treatment Implementation:is based on the treatment being implemented reliably enough to know it was the cause of effects
Statistical Conclusion Validity
A small sample may contribute to a low
statistical power.
Low Statistical power means that it is less likely that the
statistical test could find statistical difference
Type I and Type II Error
SummarySelect the appropriate statistic
Enter the data
Interpret the results
Enhance your analysis through data displaysRemember that inferential analyses of experimental research lead to statistical levels of significance, not necessarily practical levels of significance (McMillian, 2004).
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