VIEWS: 95 PAGES: 3 POSTED ON: 2/12/2010
Psych 361: Research Experience in Psychology Analyzing Your Data and Writing a Results Section These are guidelines. Please discuss these issues with your faculty mentor, as s/he may have different or additional suggestions, strategies, and means of evaluation. 1. Eyeball your raw data Look through questionnaire responses, videotapes, interview transcripts, or whatever other raw data you have to get a sense of the actual responses your participants gave. Try to get a thorough sense of the types of responses, the range of responses, and the pattern of responses across items/measures. 2. Enter and CHECK your data entry and computations Check your data entry thoroughly—that means every cell of data! If possible, find a classmate to partner up with, to check each other’s data entry, or work together on the checking (one person reads from the raw data, the other confirms with the entered data). Some of the suggestions below also catch data entry errors, but not everything. Check the accuracy of all of your computed variables (average scores, composite scores, reverse- coded variables, etc.) – compute a few by hand for each variable and check against the numbers in the dataset. Also check over the computation syntax/code carefully to make sure it is correct. Run basic descriptive statistics on each variable you will be using in your analyses, both for the whole sample and for any groups (sex, age, treatment condition, etc.) . - Do the means make conceptual sense? Are there group differences in means where you expected, and no differences where you expected none? - Are the data very narrow or wide in spread (standard deviation, range)? Do groups differ in spread? - Look at the item ranges of each variable in your dataset (not just computed variables you’ll use in your analyses – each single variable) to make sure nothing was entered out of range. For example, if a scale goes from 1-7 and during data entry someone accidentally typed 77 instead of 7 (this happens quite commonly!), this could drastically affect your results. A number out of range could also indicate that the data entry got shifted down a cell, which could mess up lots of your data, not just one variable. - Check the n for each individual variable – for every missing piece of data, find out why. Also read Chapter 11 handout: ―Collecting, Managing, and Analyzing the Data,‖ (from Dissertations and theses from start to finish; reference below). 3. Eyeball your data in the computer Look at the distribution (scatter plot) of each individual variable you will be using in your analyses. Colleen Conley ~ Knox College - Is it normally distributed or skewed? What does it mean for the variable to be normal or to be skewed? Is it expected or problematic? Note that non-normally distributed data are not suited for certain statistics because these statistics rest on the assumption of normality. - Is the distribution narrow or wide? Again, think about the meaning of this. - Look for outliers and look into each individual outlying subject/case – investigate what is going on and determine if the outlier should be thrown out. Discuss this with your faculty mentor. Plot each relevant pair of variables (e.g., Education x Income) to get a sense of the relationship between them. How strong is the relationship? Is it linear or not? Positive or negative? Make a correlation matrix of all of your relevant variables and examine the various relationships and patterns. Get a good sense of the demographics of your sample, and how demographic information (age, sex, race/ethnicity, educational status, region, etc.) relates to your central variables. You may want to plot each of your central variables against each major demographic variable (e.g., how does depression vary with age, sex, race/ethnicity, and educational status?). 4. Select appropriate statistics Start with your research questions – what do you want the data to answer? Read Chapter 10, “Selecting the Appropriate Statistics.” It is very helpful! Review your text and notes for your statistics and research methods courses to help understand which statistics are appropriate and why. Discuss appropriate statistics (and methods for doing them) with your faculty mentor. Make sure you understand how your statistics address your research questions! This is very important, so be sure to discuss this with your faculty mentor. 5. Analyze your data You will probably use SPSS or some similar program to analyze your data. If you need a tutorial on using your statistics analysis program, ask your faculty mentor. If you are still having trouble, you can ask your course instructor or a classmate for help. Note that you will likely come up with additional analyses to do as you start analyzing your data. Be prepared for this – build in extra time for this and other potential problems/complications that may arise. 6. Check your analyses Spot-check your analyses by doing a few computations by hand (if possible) and comparing with the results you obtained. Do the results make conceptual sense? Don’t just accept them at face value – think about them! If you find a significant effect, make sure you know what direction the effect is in – it could be in the opposite direction you predicted! Colleen Conley ~ Knox College 7. Write your Results section In the Results section you present the actual findings – you give your conclusions about your questions/hypotheses/predictions. You do not interpret these findings until the Discussion. Decide the best way to present your findings: In text, in tables (if you have a lot of data that can be easily summarized, such as a correlation matrix or a regression or ANOVA table), or by figure (e.g., for depicting the nature of an interaction, or showing group differences). As for the actual writing, the general approach is to state what you found and then present the data/analyses supporting your conclusions (be very clear what statistical approach you used). There is not too much room for creativity, but there are a couple of different approaches: - Write the results as a narrative (explain what you found) and use statistics to support the narrative [e.g., ―Eighth graders were significantly more depressed than sixth graders, supported by a main effect of age on depression; t(140) = 2.97, p<.05.‖]. - State up-front what statistical result you found, then elaborate [e.g., ―There was a main effect of age on depression; t(140) = 2.97, p<.05. Eighth graders were more depressed than were sixth graders.‖]. There are many technical details involved in presenting your results, and we will not go into them here. You will be responsible for following APA style for your final thesis and drafts. Faculty mentors do not want to spend the bulk of time on your drafts correct APA style errors, so do not count on them to do so. You will probably find that you will rely on the manual so much that it is worthwhile to buy your own copy. Make sure you use correct APA style as you go; don’t just save the formatting for the end (though you should set aside some time in the end to meticulously check APA style). APA style errors will be considered in your paper evaluation. Read Chapter 12 handout, “Presenting the Results,” for more detailed information. Other resources: American Psychological Association (2001). Publication manual of the American Psychological Association (5th ed). Washington, DC: American Psychological Association. Also see: http://webster.commnet.edu/apa/apa_index.htm Bem, D. J. (2003). Writing the empirical journal article. In J. M. Darley, M. P. Zanna, & H. L. Roediger III, (Eds.) The compleat academic: A career guide. Washington, DC: American Psychological Association. Full text: http://www.psych.cornell.edu/dbem/writing_article.html Cone, J. D., & Foster, S. L. (1993). Dissertations and theses from start to finish: Psychology and related fields. Washington, DC: American Psychological Association. Your Research Methods and Statistics course materials. Our expectation: When reviewing your drafts and final theses, we will expect that you have read these resources and followed the guidelines they offer. We want to focus our draft-reading on fine-tuning your writing, not on guiding you through the kind of information you can get from these sources. Colleen Conley ~ Knox College
"Analyzing Your Data"