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CHAPTER 6

CHAPTER 6. Analyzing Data. Craig A. Mertler SAGE Publications, 2012. Action Research: Improving Schools and Empowering Educators. Qualitative Data Analysis Techniques. Qualitative data are analyzed inductively

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CHAPTER 6

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  1. CHAPTER 6 Analyzing Data Craig A. Mertler SAGE Publications, 2012 Action Research: Improving Schools and Empowering Educators

  2. Qualitative Data Analysis Techniques • Qualitative data are analyzed inductively • Specific observations  look for patterns  develop hypotheses  develop general conclusions • Potentially overwhelming task • Goal is to reduce volume of information collected • Risk minimizing, simplifying, distorting data • Must rely on a coding scheme—system for grouping data into categories of similar information • Highly individualized type of system…

  3. Qualitative Data Analysis Techniques Often necessitates reading, rereading, rereading again your data Must get to ‘know’ your qualitative data very well Steps in the process: Reduce amount of narrative data through use of coding scheme Describe main characteristics of categories (connect data to research questions) Interpret what has been simplified and organized

  4. Qualitative Data Analysis Techniques Also, engage in introspection Reflective practice that helps to ensure that you remain objective and ‘emotionally unattached’ to data Assistance with analysis through software Analysis of qualitative data cannot be ‘done’ on the computer (due to inductive nature) Software can help store and organize data

  5. Quantitative Data Analysis Techniques Quantitative data are analyzed deductively Identify topic  focus with research questions or hypotheses  collect and analyze data  develop conclusions Can use either descriptive or inferential statistics Descriptive statistics—procedures that simplify, summarize, and organize numerical data Inferential statistics—procedures used to determine how likely given statistical results are for an entire population based on a sample

  6. Quantitative Data Analysis Techniques Descriptive statistics Measures of central tendency—single value to indicate what is typical or standard about a group of individuals Mean Median Mode Measure of dispersion—single value to indicate how scores are different, or what is atypical Range Standard deviation

  7. Quantitative Data Analysis Techniques Descriptive statistics (cont’d.) Measures of relationship—statistical measure of strength of association between variables Correlation coefficients

  8. Quantitative Data Analysis Techniques Descriptive statistics (cont’d.) Visual displays of data—not really a statistical procedure; simply ways to ‘show’ data Frequency distribution table Histograms Bar charts Pie charts

  9. Quantitative Data Analysis Techniques Inferential statistics Determination of how likely a given statistical result is for an entire population, based on a sample of that population Pre-set alpha () level—how much of the time would the results be due only to chance (typically equal to .05) Compare to probabily level (p-value)—results from the analysis Rules for interpretation: If p < , the difference is statistically significant; decision is ‘reject the null hypothesis’ If p > , the difference is not statistically significant; decision is ‘fail to reject the null hypothesis’

  10. Quantitative Data Analysis Techniques Inferential statistics (cont’d.) Common types of inferential analyses: Independent-measures t-test Repeated-measures t-test Analysis of variance (ANOVA) Chi-square test Statistical significance versus practical significance

  11. Quantitative Data Analysis Techniques ‘Analyzing’ standardized test data Norm-referenced scores—student performance is compared to performance of other, similar students Criterion-referenced scores—student performance is reported in terms of number of questions attempted, number answered correctly, etc. Numerous types of scores exist, including: Standard scores Grade equivalent scores National percentile ranks • Normal curve equivalent scores • National stanine scores

  12. Quantitative Data Analysis Techniques Statistical software Numerous software packages exist; some are very costly Very effective, Web-based alternative: StatCrunch (www.statcrunch.com)

  13. Reporting Results of Analyses Some general rules of thumb exist Reporting results of qualitative data analyses Must convert massive amounts of narrative data into something easily digested by readers Try to be impartial Include references to yourself, where warranted Take readers along ‘on your journey’ Include representative samples to enhance your presentation Place interesting, but nonessential, information in appendices

  14. Reporting Results of Analyses Reporting results of quantitative data analyses General guidelines: Suggestions for expressing data as numerals (APA Manual) Suggestions for expressing data using words (APA Manual) Report numerical data in descending order Report total numbers before reporting numbers in categories Use tables to organize large amounts of numerical data Use figures to present results visually

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