Where Do I Find the Values for
the F-Statement? back to Experimental Homepage Recall that when you are writing up a results section you
want to cover three things: Below you will find descriptive information and an analysis of variance summary table. This table is from an experiment that investigated whether physically attractive vs. unattractive defendants in a criminal case would be rated differently on amount of guilt (GUILTY) and length of prison sentence (PRISON). Because there is only one independent variable (attractiveness of the defendant), this analysis is referred to as a one-way analysis of variance. If there were two independent variables, then the analysis would be referred to as two-way analysis of variance.
A good results section for the analysis on guilt ratings would be: Results The effect
size r was calculated for all appropriate analyses
(Rosenthal, 1991). Guilt Ratings (Margin headings are useful to tell the reader what the paragraph will be about. Format it correctly). A one-way
analysis of variance (ANOVA) was calculated on
participants' ratings of defendant guilt. The analysis was
not significant,
If the "Guilty" analysis had been significant, then it would be correct to describe the mean differences in the following manner:
Participants
who read about an unattractive defendant rated the
defendant more guilty ( attractive
defendant ( Try writing the results for the analysis on length of prison sentence ratings...I'll get you started. Length of Prison Sentence Ratings (Margin headings are useful to tell the reader what the paragraph will be about. Format it correctly). A one-way
ANOVA was calculated on participants' ratings of length of
prison sentence for the defendant. The analysis was
significant,
Once you understand the results from a one-way ANOVA, try to figure out a more sophisticated ANOVA by clicking here.
The information contained in the "
The information that comes after the "=" is the actual
value of that
Simple. All you need to do to determine whether that particular analysis is significant is to, again, look at the analysis of variance summary table under the "Sig." column. The "Sig." column is your probability level for that particular analysis. Remember, any value in this column that is LESS than .05 is significant. All other values in that column that are greater than .05 are NOT significant. But, I KNOW you remember all of this from your statistics class...right?
"
Below you will find descriptive information and an
analysis of variance summary table. This table is from an
experiment that investigated whether the type of music
that song lyrics were attributed to would differently
impact whether participants thought the lyrics were
objectionable (OBJECT) and whether they thought the lyrics
should have a mandatory warning label (WARN). This
analysis differs from the one above, because the
independent variable (type of music) has three levels.
When you have an independent variable that has three or
more levels, then you must run comparisons among the
levels (e.g., country vs. rap, country vs. heavy metal,
rap vs. heavy metal) for each of the dependent variables.
The write-up for the lyric objection results could be as follows:
The effect
size Objection to the Lyrics A one-way
analysis of variance (ANOVA) was calculated on
participants' ratings of objection to the lyrics.
The analysis was significant,
heavy metal ( the country music condition, 1.58,
To make sure you understand, you should write up the
results for whether the lyrics should have a mandatory
warning label (WARN). Correlational
Analyses Suppose we wanted to examine
the relationship between self-esteem and negative
mood. First, we should remember that scores on the
Rosenberg self-esteem scale range from 10 to 50 with
higher scores indicating higher self-esteem. The
measure of negative mood ranges from 12 to 60 with
higher scores indicating more negative mood. We
get 122 undergraduates to complete both measures, enter
the data, run the analysis, and get the following:
How do we write this up in a results section? Results
A correlational analysis was conducted to
examine the relationship between negative mood and
self-esteem. The
analysis was significant,
r(120) =
-.49,p < .001. Participants
with
higher self-esteem scores reported less negative mood.Correlation
Statement Spacing
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Same spacing applies for F statements. |