4. Once we have…
Chosen research question
Completed literature review
Selected appropriate research design
▪ Survey; Experiment; Observation; Content Analysis
Gathered data
We can use statistics to quantify our results
for presentation and publication
5. Understand your own and others’ results
What do resulting numbers mean?
Awareness of statistical results
Remember “innumeracy?”
6.
7. Array of rows and columns that stores
observed values and variables
Similar to Excel Spreadsheet
What goes into these cells?
Review different types of variables
8. Nominal: Classification of observations into categories.
Examples: Religious Faith, Race
Ordinal: Observations can be compared by having more or less
of a particular attribute; uncertainty of equality.
Example: Olympic Performance (Gold, Silver, Bronze medals)
Interval: Intervals between values assigned to observations
have meaning and no meaningful zero point.
Examples: Temperature; Dates
Ratio: Interval variable properties with true zero point.
Examples: Income;Years of Education
9.
10. Table showing number of observations and
each value of a variable
“Lists” each variable’s possible values and
how often each occurs
11. Raw Frequency
Number of observations of a given variable
Relative Frequency
Number that transforms raw frequency into proportion or
percentage
▪ Proportion
▪ Percentage
Cumulative Frequency
Portion of total that is above or below a certain point
12. Too Much
Influence
Frequency Proportion Relative
Frequency
Cumulative
Frequency
StronglyAgree 333 .34 33.5 33.5
Agree 533 .54 53.6 87.1
Uncertain 38 .04 3.8 90.9
Disagree 75 .08 7.5 98.4
Disagree
Strongly
16 .02 1.6 100
Totals 995 1.01 100
13.
14. Describe characteristics or properties of a
set of numbers
Two MainTypes:
Measures of CentralTendency
Measures of Dispersion
15.
16. MEAN
Locates the middle or
center of a distribution
Most familiar measure of
central tendency;
“average”
Add values of variable
and divide total by total
number of values
MEDIAN
Divides distribution in half
Odd-Numbered Set
Even-Numbered Set
Most appropriate with
ordinal-level data
17. Commonly used when dealing with nominal or
categorical data
Category with the greatest frequency of
observations
If distribution has one mode = unimodal
If distribution has two modes = bimodal
If distribution has many modes = multimodal
18.
19. No variability (all scores have same value),
then variability = 0
Measure will always be positive number
(cannot be “less than zero” variation)
Greater variability of data, larger the measure
20. Largest (maximum) value
of variable minus smallest
(minimum) value
INTERQUARTILE RANGE
Divide observations into
four equal portions
First batch contains 25% of
cases, 2nd would have 25%,
and so would the 3rd and 4th
grouping; division points
are called quartiles
Finding range, but using 3rd
quartile (Q3) as maximum
and 1st quartile (Q1) as
minimum values
RANGE
23. VARIANCE
Measures variability of
distribution
Accounts for amount
which each case differs
from the mean
Extent of variance
correlates to size of overall
range of numbers
STANDARD DEVIATION
Looks at how far from mean a
group of number is
Higher Standard Deviation =
Data points further from mean
Lower Standard Deviation =
Data points close to mean
24.
25.
26. Displays distribution of one variable for each
category of another variable
Steps to Creating Cross-Tabs:
#1: Record respondents’ answers to question
#2: Create categories for table
#3: Count number of respondents who fall into
each category
#4: Convert tallies to frequencies;
add up row and column tables
27.
28.
29. Statistical technique centered on
expressing relationship between two
quantitative variables with a linear equation
Idea of “Best Fit Line”
Correlation Coefficient (r)
Coefficient of Determination (R2)