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Writing up results

Writing up results. Results are divided into two main sections , usually. Descriptive Statistics Include frequencies for nominal/categorical variables Include means and standard D for continuous/ranked variables Include # of missing (not items, but actual surveys that could not be included ).

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Writing up results

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  1. Writing up results

  2. Results are divided into two main sections, usually • Descriptive Statistics • Include frequencies for nominal/categorical variables • Include means and standard D for continuous/ranked variables • Include # of missing (not items, but actual surveys that could not be included)

  3. Some uncertainties • Descriptive statistics (means, sd) are often compared to similar published data to see if they make sense. • E.g. “CES-D scores had a mean of 8, SD of .79 in the current sample. This is similar to a same aged sample collected by Smith and Jones (2011). • This kind of info can go in results or sometimes in discussion. I prefer it in results. • You don’t have to include it, but if you do that is where I would like it.

  4. Where do the alphas go? • Alpha tells us if folks answered questions consistently within the same scale • The alphas go in the method section • Technically they are a “result,” but they go in the method section at the end of the description of the measure.

  5. How do you calculate an alpha? • In SPSS • Go to Analyze/Scale/Reliability Analysis • Select the items in the scale (use the reverse scored items where relevant) • Analyze • Results show if your scale has good internal consistency in this sample or not

  6. Results—substantive analyses • If you have hypotheses, you can call this section “hypothesis testing” • Otherwise you can call it “Research Questions” • Looking at how other published articles do this is the simplest, fastest way to learn how to present these numbers.

  7. Results Section General Framework • Results for each hypothesis can be divided into 3 general statements • 1. What the test was, generally • 2. The actual variables and setup of the analysis • 3. The numerical and substantive outcome of the analysis

  8. 1. mention the type of statistical test you used • Like “We ran correlation analyses to determine if happiness total score was related to the number of reported facebook friends.” • Or “We used analysis of covariance to determine if there were differences in food consumption by experimental or control group, after controlling for body image ratings.”

  9. Results sections • Or • “We used correlation to determine if self-reported frequency of drug use was related to perception of peer risk behavior.”

  10. 2. Specify the variables and setup • This will sometimes seem redundant to the previous statement. If so, you can leave it out or minimize it. • Like “We calculated bivariate correlations between the total score for self reported drug use, and perception of peer drug use.” • This is redundant. You could leave it out.

  11. 2. Specify the variables • It will not always seem redundant. • Like “We entered group membership (experiment or control) as a predictor, and body image rating as a covariate. The dependent variable was ounces of unhealthy snack food consumed.

  12. 2. Specify the variables • Sometimes you will have repetition when you are doing analyses that are parallel. • Like “We conducted a separate ANCOVA with the same predictor and covariate, and the dependent variable for this analysis was ounces of healthy snack food consumed.” • Be sure to specify when the dependent or independent variables change for various analyses, even if they are related

  13. 3. Give the numerical and substantive outcome • The numbers will depend on your test. You need to include the statistic and the significance (p value, usually). • E.g. for a correlation: “self perceived drug use and perception of peer drug use were significantly positively related: r = .14, n=78, p=.04.” • Or “Happiness totals and intensity of facebook use were significantly negative correlated: r=-.20, p=.03.”

  14. …numerical and substantive outcome • If you are presenting ANOVA, ANCOVA or T tests you will also need to include the degrees of freedom. • E.g. “There was a main effect for group membership, such that members of the experimental group were less likely to consume unhealthy snacks: F=3.22 (1, 68), p=.02. Means and standard deviations are presented in Table X.

  15. Numerical and substantive outcome • In a real paper you do not report the numbers for non-findings • In this paper I would like you to report the numbers for non-findings • Also note that statistics such as r, t, F, and p are italicized in apa style.

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