Organizing and Displaying Data:

How to make sense of our data?
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When researchers collect data, he/she has large quantities. Therefore you must simplify it.
One method is:

Frequency Distribution
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Systematic method of ordering, organizing and displaying data from a set
Purpose
:
1.
Simplifies calculations for other statistics
2.
Transition step in constructing a frequency histogram
Types of Frequency Distributions:

Ungrouped
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Each value of x in the distribution represents one value in the data (used if you have a
category for each possible value, i.e nominal scores OR if you have a small dataset)

Grouped
(class intervals):
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Several values in the data are classified into one interval (i.e. used if you have a large
dataset, which is more typical
Steps in Constructing a Frequency Distribution:

Seven steps in constructing a frequency distribution when the data are interval/ratio.
1.
Count the number of scores
2.
Identify highest and lowest score
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Organize the scores
3.
Identify smallest unit of measurement
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What is the smallest division (possible) that was used on the measuring scale
when the scores were collected? (i.e. by how much can your score increase from
one participant to another?)
4.
Decide on appropriate number of class intervals
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Use the following rule (Modified Sturge's Rule). This is only a guide
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But if you have only a small number of unique values in your dataset, use this
unique (ungrouped) values to determine the number of class intervals (so skip
Step 4 & 5).
5.
Decide on the score range of each class interval (i)
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Use the following formula where:

i = width of class interval

i = largest score  smallest score
Number of class intervals
6.
Round to a nice number
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As a general rule "i" equal to 1, 2, 3, 4, 5, 10, or 20 score divisions will be
suitable. (Pick a "nice" number!).
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For example, if instead of 5, we had 6 intervals (so 6 in the denominator), i =
0.833. Better i = 1
7.
List class intervals of scores in order
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Usually the largest interval is put at the top