How to Write a Results Section by J4v6Jb6

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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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