Analyzing data Chapter 6 Action Research: Improving Schools and Empowering Educators (4/e) Craig A. Mertler SAGE Publications, 2014

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 Groups Measure Final Exam Score Ms. Sizemore's Class Mr. James' Measures Group Mrs. Love's Class Unit Pretest Unit Posttest Groups Measure Statewide Achievement Test Scores Westside School Central School Eastside School

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.