A Short Guide to Action Research4th Edition
Andrew P. Johnson, Ph.D.Minnesota State University, Mankato
www.OPDT-Johnson.com
Chapter 8: Quantitative Design in Action Research
• 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. causal–comparative research
3. quasi-experimental research
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
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
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
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
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
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
Five Quasi-Experimental Designs
• Exp = experimental group• Cnt = control group• O = observation or measure• T = treatment
Pretest-Posttest Design
Group Time
Exp O T O
Cnt O — O
Pretest-Posttest Group Design
Group Time
Exp O T O
Cnt O — O
Time Series Design
Group Time
Exp O O O O T O O O O
Group Time
Exp T1 O O O O T2 O O O O
Time Series Group Design
Group
Time
Exp O O O O T O O O O
Cnt O O O O — O O O O
Group
Time
Exp T1 O O O O T2 O O O O
Cnt T1 O O O O T1 O O O O
Equivalent Time-Sample Design
Group Time
Exp T O — O T O — O
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
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
Line graph for frequency distribution
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
Scores with a Small Variance
xx xxxxxxx
xxxx
xxxxxx
xxxx
xxx
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
Small Standard Deviation: Closely Distributed Scores
Large Standard Deviation: Widely Distributed Scores
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
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