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Brian's Class Materials- SPRING 2008 - SEYS 778

SEYS 778 Home

Spring 2008

 SEYS 778 – Seminar Research in Science Education II

Wednesday 7:10 pm to 9:40 pm

Powdermaker Hall Room 020

Data Collection (II) – Analysis and Figures, Tables

Much thought must be put into selecting the best way to analyze the data you collect, because it is through a well structured analysis that outcomes become clear and most importantly, defensible. Therefore,  analyzing and summarizing the data must show the outcomes are dependable, accurate, reliable, correct.  It is an attempt by the researcher to find meaning, to answer the question, “So what?”

A. First attempts to analyze data:

1. Look for patterns, try to identify themes (eg. Any data that accumulates and is

    repetitive,  key trends,  common phrases used in free-response statements).

2.  Define a system for coding information/data if not established earlier in

     the study. For example, using a Likert scale in a questionnaire already establishes

     set ways to analyze and report data.   Interval data such as test scores are often

     used to document outcomes.  

3.  Try concept mapping to help you visualize the relationships that emerge,

     particularly in action research-type studies.

4.  Consider whether your analysis should employ statistics.  Descriptive statistics

measures central tendency – the mean (the average), median (middle score), mode (most frequently occurring score(s). Variability indicates how spread out a group of scores are – Standard deviation is a measure of distance from the mean that helps define how much a particular score deviates from the average. Relative standing- Percentile ranking,  Correlation between 2 or more factors?

5.  Inferential statistics are used to determine how closely samples match the population

     at large.  Statistical analysis is run on data to find relationships that are deemed

     “significant.” These include tests of significance of correlation, difference between

     means, analysis of variance and chi- square for non-parametric statistics.

B. Presentation of results:

1.   Consider what format would best illustrate the results of the data analysis.  What

      presents the clearest picture … grids, graphs, pictures, diagrams, charts, tables of


2.   All illustrations are titled in a final report.  Most are embedded in the body of the

       paper or put in the appendix.   See examples from text.

C. Reflecting on the results of A. above to reach conclusions and implications.

1.  An important aspect of analysis is finding implications of the data when the analysis

     is complete.  What do the findings suggest?  What questions remain open to further

     investigation?   Avoid unwarranted assertions based on sloppy analysis of thin data.

2.  Consider the role of theories related to your area of research to help interpret data


WORKSHOP GOALS:  Find a group in which you can discuss mutual concerns.

Working groups:

A.   Instructional innovations and student attitudes/achievement

B.  Innovative” programs – Case studies and descriptive reports

C.  Other

The goals for this workshop are to help you resolve study issues that pertain to the following questions:

1. How will I tabulate the data I collect?

In a prior workshop, tabulation was discussed.  Tabulation of data requires you to define how you will organize and categorize the data you collect.

2. How will I analyze the data once it is tabulated? 

    Does the data need to be summarized using descriptive statistical measures, such as

    averages, or means?   What other questions are being looked at?  

3.  How will I report the outcome(s) of my study?