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Analyzing data. Chapter 6. Craig A. Mertler SAGE Publications, 2014. Action Research: Improving Schools and Empowering Educators (4/e). Qualitative Data Analysis Techniques. Qualitative data are analyzed inductively

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## Analyzing data

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**Analyzing data**Chapter 6 Craig A. Mertler SAGE Publications, 2014 Action Research: Improving Schools and Empowering Educators (4/e)**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**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**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**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**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**Quantitative Data Analysis Techniques**• Descriptive statistics (cont’d.) • Measures of relationship—statistical measure of strength of association between variables • Correlation coefficients**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**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 probability 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”**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**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**Quantitative Data Analysis Techniques**• Statistical software • Numerous software packages exist; some are very costly • Very effective, Web-based alternative: StatCrunch (www.statcrunch.com)**Mixed-methods Data Analysis Techniques**• Explanatory mixed-methods: • Quantitative data analyzed first, followed by qualitative • Interpretation of qualitative results should focus on extension, elaboration of quantitative results • Exploratory mixed-methods: • Qualitative data analyzed first, followed by quantitative • Interpretation of qualitative results should should lead to or inform collection and analysis of quantitative data • Triangulation mixed-methods: • Quantitative data and qualitative data are analyzed simultaneously**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**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**Data analysis template**• Planning for Data Analysis**Action research checklist 6**Action Research Checklist 6: Analyzing Data in Action Research ☐ Revisit your research question(s) and your previous decisions about the use of qualitative, quantitative, or mixed-methods data for your action research. ☐ Develop a plan for analyzing your data (see below). ☐ If you have collected qualitative data, decide how you plan to analyze your data: ☐ Will you code, organize, and analyze your data by hand? ☐ How will you actually do this (notecards, sticky notes, etc.)? ☐ Will you use some sort of software (see the “Related Websites” section of this chapter) to code, organize, and analyze your data? ☐ If you have collected quantitative data, decide how you plan to analyze your data: ☐ Be sure to specify the type of analysis—descriptive (e.g., frequencies, mean, median, graphs, etc.) or inferential statistics (e.g., t-test, ANOVA, chi-square test, etc.)—you plan to use. ☐ Will you analyze your data by hand, perhaps using only a calculator? ☐ Will you use some sort of software (e.g., StatCrunch, or others in the “Related Websites” section of this chapter) to analyze your data? ☐ Anticipate how you will present the results of your data analysis: ☐ Will you present all of your results in narrative form? ☐ Will you utilize any tables, graphs, etc.? ☐ Develop a timeline for your data analyses.

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