A Short Guide to Action Research 4 th Edition

1 / 24

A Short Guide to Action Research 4 th Edition - PowerPoint PPT Presentation

A Short Guide to Action Research 4 th 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

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

A Short Guide to Action Research 4 th Edition

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

A Short Guide to Action Research4th Edition

Andrew P. Johnson, Ph.D.

Minnesota State University, Mankato

www.OPDT-Johnson.com

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 possiblein 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

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
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

Scores with a Large Variance

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