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Chapter 2

Chapter 2 Part I

Course Code
Shirin Montazer

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Frequency Distributions of Nominal Data
๎€The whole idea is to transform raw data into a meaningful and organized set of
measures that can be used to test hypothesis
๎€The first step is to construct a frequency distribution in the form of a table
๎€Ex: Question- Response of young boys to frustration
Table 2.1 Responses of Young Boys to Removal of Toy
Response of a Child Frequency f
Express Anger15
Play with Another Toy5
(Total) N=50
๎€Every table must have title and label by numbers as well
๎€Frequency distribution of nominal data consist of two columns consisting of
oCharacteristics presented (response of child)
oCategories of analysis(cry, anger, withdraw)
oFrequency (# of responses per category)
oTotal number of responses right at the end (N = 50)
๎€Looking at this table, it is evident that more young boys respond by crying or with
anger than withdrawal or playing with another toy
Comparing Distributions
๎€Making comparisons between frequency distributions is often used to clarify results
and add information
๎€Ex: Table 2.2 Response to Removal of Toy by Gender of Child
Gender of Child
Response of a Child Male Female
Cry25 28
Express Anger15 3
Withdraw5 4
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Play with Another Toy5 15
N=50 50
๎€As shown in table 2.2, 15 out 50 girls but only 5 out of 50 boys responded by playing
with another toy in the room
Proportions and Percentages
๎€When a researcher studies distributions of equal size, the frequency data can be
used to make comparisons between the groups
oEx: 50 girls and 50 boys
๎€The most useful method of standardizing for size and comparison distributions are
the proportion and percentage
oProportion: compares number of cases in a given category with the total size
of the distribution
๎€P= ๎€‚๎€ƒ
๎€P= ๎€„๎€…๎€…๎€†
oPercentage: multiply any given proportion by 100
๎€% = (100) ๎€‚๎€ƒ
๎€% = (100) ๎€„๎€…๎€…๎€†
๎€% = 30
๎€Thus, 30 % of girls find alternative toys to play with
๎€To illustrate the utility of percentage s in making comparisons with large and
unequal-sized distributions : Table 2.3
oCollege A has 1.352 engineering majors
oCollege B has 183 engineering majors
Engineering Majors
College ACollege B
Male 1,082 80 146 80
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