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How to Write a Results Section APA manual resources: Section 1.10 (on results) Section 3.53 - 3.61 (on presenting statistics) Section 3.62 – 3.74 (on tables) Section 3.75 – 3.86 (on figures, which includes graphs) In the results section you are presenting the findings of your experiment. Although you do not want to get into lengthy discussion of theoretical issues in your results section, you can provide some explanation of what the results mean. A good pattern is to report the results in statistical language, followed by a statement in English about what that means. Your results section may be very brief, or it may go on for several paragraphs. Some experiments require you to do more than one set of analyses – put each set in a separate paragraph. All results sections should begin with a statement about how you reduced the data, and then refer to a table or figure where you present the data itself. For example, in a typical RT experiment there are many trials, but those are reduced to the means for each condition for each subject. Did you eliminate any subjects at this stage for having error rates that were too high or other reasons that make their data suspicious? Report them here. Present the actual data in a table or a figure. In an actual paper you would never use both, but sometimes I will require both to give you lots of practice. There are no standards on the reporting of statistics. I would like you to report exact p values, to 3 decimal places, unless spss tells you that p = .000, in which case report that p< .001. 1. For a t-test (either independent or paired) Start with a description of your data. Although you would never use a table or figure to report just 2 numbers, I will have you do so for practice (normally they would just be reported in the text). Then report the results of the t-test, followed by an English statement of which mean was the higher (a significant t-test just tells you that two means are different, it doesn’t tell you which one was higher). Alternatively, you can start with the English language statement, and then back it up with the statistics. Number of items recalled in each encoding condition was compared with an independent t-test. There was a significant difference between conditions, t(32) = 2.95, p = .03. More words were recalled in the semantic encoding condition than in the phonological encoding condition (see Table 1). OR Mean number of items recalled were calculated for the semantic and phonological encoding condition, and are presented in Table 1. More words were recalled in the semantic encoding condition than in the phonological encoding condition, t(32) = 2.95, p = .03 2. For an ANOVA There are many types of ANOVA, but they all have the same basic format: there are 2 or more factors (independent variables), each of which has 2 or more levels. The factors can be either within-subjects or between-subjects. a. Like any results section, start with a statement about how you took the data that you collected and prepared it for analysis. Then present the data to your reader, either in a table or a figure. Eg. Mean response times were calculated for each condition, and are presented in Table 1. b. Then you need to introduce your ANOVA. In this sentence, you are going to present your design. Mention each factor, and the levels of each factor. If all of your factors are between subjects, you can call it a factorial ANOVA. If all of your factors are within-subjects, you can call it a repeated measures ANOVA. If you have some of each, you call it a mixed ANOVA, and then you go on to say which factors are within and which factors are between. E.g., Response times were analyzed in a 2 (encoding: shallow, deep) x 2 (modality: auditory, visual) mixed Analysis of Variance (ANOVA), with encoding as a within-subjects factor and modality as a between-subjects factor. c. Then you report your main effects, one at a time. In a very complex design (e.g., in a 2 x 2 x 2 x 3 design there are 4 main effects, 6 2-way interactions, 4 3-way interactions, and 1 4-way interaction) you might report only the significant main effects, and the theoretically-interesting nonsignificant effects. However, since you will be doing only very simple designs, report all of your main effects and all of your interactions, whether they are significant or not. When you report the effect, first describe the effect in statistics, then in English, or, if you are comfortable doing so, you can combine them. E.g., There was a main effect of gender, F(1, 23) = 3.16, p = .022, such that women were funnier than men. OR Women were funnier than men, F(1, 23) = 3.16, p = .022 If the interaction is significant, you need to check to see if the main effect is still valid (sometimes it is, but sometimes it isn’t) If the main effect is misleading (i.e., the effect holds for one level but not the other), you need to qualify it, so that your reader knows not to be fooled by it. E.g., There was a main effect of regularity, F(1, 31) = 5.67, p = .01, that was qualified by the frequency x regularity interaction. Then you would go on to describe the interaction (see below). d. Then you describe the interaction. If it’s not significant, this is easy – just say that it’s not significant, and report the F (you can report the exact p, or you can report ns, which stands for not significant). If the interaction is significant, report it, and then describe it in English. E.g., There was an interaction between word frequency and regularity, F(1, 31) = 5.67, p = .008. For high frequency words, response times were the same for regular and irregular words however, for low frequency words, response times were greater for irregular than for regular words. Sometimes an interaction occurs when both levels show the same pattern of results, but the effect is greater for one than the other. E.g., There was an interaction between word frequency and regularity, F(1, 31) = 5.67, p = .008, such that irregular words produced greater slowing for low frequency words than for high frequency words.
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