Chapter 6 Analyzing Data QUALITATIVE DATA ANALYSIS QUANTITATIVE

Chapter 6: Analyzing Data QUALITATIVE DATA ANALYSIS QUANTITATIVE DATA ANALYSIS

Analyzing Data: Qualitative Data Analysis Techniques Inductive Analysis Using Computer Software Guidelines For Reporting

Analyzing Data: Quantitative Data Analysis Techniques Descriptive Statistics Inferential Statistics Using Computer Software Guidelines for Reporting

Qualitative Data Analysis Techniques: �An inductive process: (1) Begin w/ specific observations (2) Note patterns (3) Formulate tentative hypothesis (4) General conclusion; theory �View phenomena from holistic perspective, factoring in setting, participants, unique context (Parson & Brown, 2002)

Qualitative Data Analysis: Inductive Analysis �Reduce large volumes of information. �Organize the data into important patterns and themes. �Being careful not to minimize, distort, oversimplify, or misinterpret data (Schwalbach, 2004). �"Systemically organizing & presenting findings. . . in ways that facilitate understanding of data. "(Parsons & Brown, 2002)

Qualitative Data Analysis: Inductive Analysis �Coding Scheme: Ways to organize categories of information; repeated words or phrases. (Mills, 2003; Schwalbach, 2003) �Knowing Your Data: Reading, rereading, process can be laborious. �Describe Characteristics of Categories �Connections between data and research questions begin to emerge.

Qualitative Data Analysis: Inductive Analysis �Reflection: Describe categories in terms of their connection to or ability to understand my research question. �Conflicting Data: Information that 'conflicts' with patterns. �Interpret: Examination of events, behaviors, or observations by category. �Introspection, Constant Comparisons.

Qualitative Data Analysis: Inductive Analysis �Using Computer Software �Keyword: 'assist' �Can help 'sort' information using electronic 'coding' scheme. �Useful for large amounts of data. �Specialized software. �May need assistance w/ process.

Quantitative Data-analysis �Descriptive Statistics: Simple, mathematical procedures used to summarize and organize relative large amounts of numerical data: (1) Measures of central tendency (2) Measures of dispersion (3) Measures of relationship

Measures of Central Tendency �Mean: arithmetic average of a set of scores. May be necessary to drop 'outliers' to get reliable mean. �Median: specific score in the data set that separates the entire distribution in equal halves. � �Mode: most frequently occurring score in a data set.

Measures of Dispersion �Measures of Dispersion: Indicate how much 'spread' or diversity exist within a group of scores. �Range: Distance between highest and lowest score. �Standard Deviation: Average distance of scores away from the mean. 'SD' impacted by 'extreme' scores.

Measures of Relationship �Correlation Coefficients: measures of direction and degree of relationship between two variables. �Strong: 1. 00 --. 70 (+ or -) �Moderate: . 70 --. 30 (+ or -) �Weak: . 30 --. 00 (+ or -) �Direction and Strength of relationship between two variables.

Visual Representations �Bar Graph �Pie Chart �Histogram �Frequency Distribution Table �Visual way to understand large amounts of data.

Inferential Statistics �Inferential Statistics: 'Infer' how likely a given statistical result from a 'sample' applies to an entire population. �Independent Measures 't' Test: used for 'two group' (treatment and control). Data are compared on a common dependent variable (such as a test score). Mean scores for two groups are compared to see if differences are 'statistically' significant. If difference is 'SS' then there is a 'true' difference btw groups.

Inferential Statistics �Repeated Measures 't' Test: More than one test score is taken on the same person in an study. �'Practical' Significance: subjective decision of significance determined by looking at practical factors. �P-value: indicates probability of chance occurrences in the study.

Inferential Statistics �Alpha level (a): typically set at 0. 05 in educational research studies. Reasonably certain that only 5% of time would differences we obtain between two 'means' be due to chance -- thus representing no 'real' difference between the groups. �If p < a than the difference is statistically significant. Why?

Inferential Statistics �Is there a Statistically Significant Difference? �Reject the Null Hypothesis: (double negative) Null Hypothesis says: NO difference between the groups. So, if we REJECT the Null -- it means THERE ARE SIGNIFICANT DIFFERENCES BETWEEN THE GROUPS -- the intervention' made a difference. (HOORAY!) �Fail to Reject the Null Hypothesis: (triple negative) = NO significant differences between the groups (project 'failed').

Inferential Statistics �Analysis of Variance (ANOVA): variation of independent 't' test: (or 'True Statistically Significant Differences’ Test: Used when there are more than two 'groups' being compared. �Chi-square Test: Used when looking at 'frequency' counts in data, not scores. �Ex: Number of boys or girls who. . .

Using Computer Software �'Stat. Crunch' -- web-based data analysis software system - Univ. of S. Carolina. �Fire Up 4. 0 Beta! link �Interactive Java window. �Feel free to 'experiment' if you are interested! �Other software available.

Reporting the Results of Qualitative Research �How do I present the results of my research most effectively? �Consider needs of audience. �Make every effort to be impartial. �Watch 'value judgments. � Keep conclusions 'tentative'. �Provide examples and samples.

Qualitative Research Format: �Introduction �Review of related literature �Description of innovation/intervention �Data collection and considerations �Data analysis and interpretations �Conclusions �Reflection and Action Plan

Reporting the Results of Quantitative Research �APA format for reporting numbers. �Present numerical information in descending order from largest to least. �Report total number before small categories are described. �Use tables to organize large sets of numbers or data. �Use graphs to illustrate numerical data.

References 1) Mertler, C. A. (2014). Action Research: Improving Schools and Empowering Educators, 3 th ed. Los Angeles, CA: Sage Publishers.