A Short Guide to Action Research 4th Edition

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A Short Guide to Action Research4th Edition

• Andrew P. Johnson, Ph.D.
• Minnesota State University, Mankato
• www.OPDT-Johnson.com

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• Chapter 8 Quantitative Design in Action Research

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• Quantitative research is based on the collection
  and analysis of numerical data
• Three quantitative research designs can fit
  within the action research paradigm
• 1. correlational research
• 2. causalcomparative research
• 3. quasi-experimental research

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• CORRELATIONAL RESEARCH
• Seeks to determine whether and to what degree a
  statistical relationship exists between two or
  more variables
• Used to describe an existing condition or
  something that has happened in the past

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• Correlation Coefficient
• Correlation coefficient the degree or strength
  of a particular correlation
• Positive correlation when one variable
  increases, the other one also increases
• Negative correlation when one variable
  increases, the other one decreases
• Correlation coefficient of 1.00 a perfect
  one-to-one positive correlation
• Correlation coefficient of .0 absolutely no
  correlation between two variables
• Correlation coefficient of 1.00 a perfect
  negative correlation

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• Misusing Correlational Research
• Correlation does not indicate causation
• Just because two variables are related, we cannot
  say that one causes the other
• Negative Correlation
• Increase in one variable causes a decrease in
  another

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• Making Predictions
• Correlation coefficient identified by the symbol
  r
• When r 0 to .35, the relationship between the
  two variables is nonexistent or low
• When r .35 to .65, there is a slight
  relationship.
• When r .65 to .85, there is a strong
  relationship

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• CAUSAL-COMPARATIVE RESEARCH
• Used to find reason for existing differences
  between two or more groups
• Used when random assignment of participants for
  groups cannot be met
• Like correlational research, used to describe an
  existing situation
• compares groups to find a cause for differences
  in measures or scores

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• QUASI-EXPERIMENTAL RESEARCH
• Like true experiment but no random assignment
  of subjects to groups
• random selection is not possible in most schools
  and classrooms
• Pre-tests and matching used to ensure comparison
  groups are relatively similar

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• Five Quasi-Experimental Designs
• Exp experimental group
• Cnt control group
• O observation or measure
• T treatment

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• Pretest-Posttest Design

Group Time ? Time ? Time ?
Exp O T O
Cnt O O
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• Pretest-Posttest Group Design

Group Time ? Time ? Time ?
Exp O T O
Cnt O O
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• Time Series Design

Group Time ? Time ? Time ? Time ? Time ? Time ? Time ? Time ? Time ? Time ?
Exp O O O O T O O O O
Group Time ? Time ? Time ? Time ? Time ? Time ? Time ? Time ? Time ? Time ?
Exp T1 O O O O T2 O O O O
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• Time Series Group Design

Group Time ? Time ? Time ? Time ? Time ? Time ? Time ? Time ? Time ? Time ?
Exp O O O O T O O O O O
Cnt O O O O O O O O O
Group Time ? Time ? Time ? Time ? Time ? Time ? Time ? Time ? Time ? Time ?
Exp T1 O O O O T2 O O O O
Cnt T1 O O O O T1 O O O O
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• Equivalent Time-Sample Design

Group Time ? Time ? Time ? Time ? Time ? Time ? Time ? Time ?
Exp T O O T O O
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• THE FUNCTION OF STATISTICS
• Descriptive statistics statistical analyses
  used to describe an existing set of data
• Measures of central tendency describes a set of
  data with a single number
• a. mode - score that is attained most
  frequently
• b. median - 50 of the scores are above and 50
  are below
• c. mean - the arithmetic average

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• Frequency Distribution all the scores that were
  attained and how many people attained each score

Scores Number of Students
99 1
97 1
92 2
90 1
85 2
84 4
83 6
80 12
79 5
78 6
75 4
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• Line graph for frequency distribution

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• Measures of variability the spread of scores or
  how close the scores cluster around the mean
• Range the difference between the highest and
  lowest score
• Variance the amount of spread among the test
  scores
• standard deviation how tightly the scores are
  clustered around the mean in a set of data

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Scores with a Small Variance
xx xx xx xxx xx xx xx xx xx xx xx xx x
Scores with a Large Variance
x x x x x x x x x x x xx xx x x x x x x x x x x
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Small Standard Deviation Closely Distributed
Scores
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• Large Standard Deviation Widely Distributed
  Scores

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• INFERENTIAL STATISTICS
• Inferential statistics statistical analyses
  used to determine how likely a given outcome is
  for an entire population based on a sample size
• make inferences to larger populations by
  collecting data on a small sample size
• Statistical significance that difference
  between groups was not caused by chance or
  sampling error