For this assignment you will need to download and open a data file that is posted in the Course Content folder and titled “descriptive dataset.sav”.
Perform analyses in SPSS on three (3) variables (one nominal, one ordinal, and one intervallevel variable) to obtain appropriate descriptive statistics (e.g., measures of central tendency, dispersion, and distribution) related to the sample. Be sure to choose appropriate descriptive statistics that correspond with the level of measurement (nominal/ordinal/interval) for each of the three variables selected. For example, only the mode, frequency, or percentage of total cases can be reported for a nominal variable.
Create data displays (e.g., histogram graphs, bar graphs, line graphs, pie charts) of related variables — that is, variables that would make sense to compare based on what it is they are measuring
Export or paste your output into a Word document. Describe the results. Identify whether data are normally distributed. (e.g., you may choose to also write about the data distribution’s skew and kurtosis.) Write up the results of these analyses as they would appear in a research report. Use the APA manual or the OWL at Purdue Writing with Statistics webpage for formatting guidance. Be sure to discuss your analyses.
Write a short paragraph that highlights your understanding of why exploratory data is a critical part of any statistical analysis. The rationale must include definitions of key concepts and course citations to demonstrate your understanding.
Additional information on how to create data displays is available in Pallant Chapter 7, found in the Course Readings folder. You must show a high level of understanding of the importance of using descriptive statistics to receive full credit on this assignment. You must also format the data displays within the body of your assignment. Be sure your SPSScreated data displays are exported and formatted properly to your Word document.
Module overview :
Chapter two will discuss computing and understanding averages, applying scales, and levels of measurement related to central tendency. Chapter three discussed the variability and tools used to compute dispersion.
Module Outcomes/Objectives:
Formulate innovative business solutions that incorporate the use of statistical software. Integrate knowledge of statistical theory and quantitative data analysis as shown in scholarly and practitioner applied business research. Construct an independent evaluation of underlying assumptions and requirements of statistical designs and techniques.
NOTE* I attached below the Salkind Chapters 2,3,4,7, also attached my classmate’s assignment as an example but make sure please all writing must be original
“descriptive dataset.sav”. Attached Files: Survey_SPSS and Chapters 2,3,4
Chapter 2
Means to an End:
Computing and
Understanding Averages
Understanding measures of central tendency
Computing the mean for a set of scores
Computing the median for a set of scores
Computing the mode for a set of scores
Understanding and applying scales or levels
of measurement
Selecting a measure of central tendency
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
The AVERAGE is one value that best
represents a set of scores
Another name for AVERAGES is measures of
central tendency
Examples include the mean, median, and
mode
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
is the mean value of the group of scores.
Σ (sigma) tells you to add together whatever
follows it.
X is each individual score in the group.
The n is the sample size.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
1. List the entire set of values in one or more
columns. These are all the Xs.
2. Compute the sum or total of all of the values.
3. Divide the total or sum by the number of
values.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
. . .
The mean is sometimes represented by the letter M.
n = sample size N = population size
Sample mean is the measure of central tendency
that best represents the population mean.
It is also called the arithmetic mean.
Mean is like the fulcrum on a seesaw.
Mean is VERY sensitive to extreme scores that can
skew or distort findings.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Step 1: List all values for which the mean is being
calculated. (List them only once.)
Step 2: List the frequency with which each value
occurs.
Step 3: Multiply the value by the frequency, as
shown in the third column.
Step 4: Sum all of the values in the Value
Frequency column.
Step 5: Divide by the total frequency.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
The median is defined as the midpoint in a
set of scores.
It’s the point at which one half, or 50%, of the
scores fall above, and one half, or 50%, fall
below.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
1) List the values, in order, either from highest
to lowest or lowest to highest.
2) Find the middlemost score. That’s the
median.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
What if there are two middle scores?
The median is simply the mean of the two
middle values.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Percentile ranks are used to define the
percentage of cases equal to and below a
certain point on a distribution.
75th percentile means that the score
received is at or above 75% of all other
scores in the distribution.
Median is always at the 50th percentile.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Why use the median instead of the mean?
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Things to Remember
The mean is the middle point in a set of
values, whereas the median is the middle
point in a set of cases.
Because the median cares about the
number of cases, extreme scores (i.e.,
outliers) do not impact it.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Mode = most frequently occurring value
This is the least precise measure of central
tendency.
When two values occur the same number of
times, there is bimodal distribution.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
List all values in the distribution, but list each
value only once.
Tally the number of times each value occurs.
The value occurring the most is the mode.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Party Affiliation Number or Frequency
Democrats 90
Republicans 70
Independents 140
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Bimodal = Distribution with two modes
Trimodal = Distribution with three modes
Trimodal distributions are unlikely when
dealing with a large set of data points, but
they are possible.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Level of measurement dictates what specific
measure of central tendency you will use.
Measurement is the assignment of values to
outcomes following a set of rules.
Each of the four levels has a particular set of
characteristics.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Nominal
Ordinal
Interval
Ratio
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Defined by the characteristics of an outcome
that fit into one and only one class or
category
These are mutually exclusive.
Examples include gender and political
affiliation.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
The characteristic of things being measured
here is that they are ordered.
Example: Ranking candidates for a job
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Based on some underlying continuum, such
that we can talk about how much more a
higher performance is than a lesser one
The intervals, spaces, or points along the
scale are equal to one another.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
An assessment tool at the ratio level of
measurement is characterized by the
presence of an absolute zero on the scale.
Examples: Zero molecular movement and
zero light
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Use mode when the data are qualitative,
categorical, or nominal (e.g., eye color or
political party) and values can only fit into
one category (i.e., mutually exclusive).
Use median when you have extreme scores.
Use mean when the data do not include
extreme scores (i.e., outliers) and are not
qualitative, categorical, or nominal.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
 Chapter 2 �Means to an End:�Computing and Understanding Averages�
What You Will Learn in Chapter 2
Measures of Central Tendency
Computing the Mean
Steps to Computing the Mean
Things to Remember . . .
Weighted Mean
Median
Steps to Finding the Median
BUT . . .
A Little About Percentiles . . .
Critical Thinking
Things to Remember
Computing the Mode
Steps to Finding Mode
Example of Finding Mode
Multimodal
Scales of Measurement
Four Flavors of Scales of Measurement
Nominal Level of Measurement
Ordinal Level of Measurement
Interval Level of Measurement
Ratio Level of Measurement
When to Use What . . .
Using the Computer: Descriptive Statistics
The SPSS Output
Chapter 3
Viva La Différence:
Understanding Variability
Understanding the value of variability as a
descriptive tool
Computing the range
Computing the standard deviation
Computing the variance
Understanding what the standard deviation and
variance have in common and how they are
different
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Why Understanding Variability
Is Important
Variability reflects how scores differ from one
another.
Also called spread or dispersion
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Three measures of variability are commonly
used to reflect the degree of variability,
including range, standard deviation, and
variance.
Typically report the average and the
variability together to describe a distribution
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Range is the most general estimate of
variability
There are two types of range, although the
most commonly used is the exclusive range.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
General formula for range
Also known as the exclusive range
Range = h − l
Where h is the highest score, and l is the
lowest score
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Inclusive Range = h − l + 1
This type of range is less commonly seen.
Where h is the highest score, and l is the
lowest score
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Most frequently used measure of variability
SD = s = represents the average amount of
variability in a set of scores
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
s = standard deviation
Σ = sigma, which tells you to find the sum of what
follows it
X = each individual score
= Xbar = mean of all of the scores in the
sample
n = sample size
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Standard deviation is an estimate of the
POPULATION standard deviation.
To make it an unbiased estimate, you must
subtract 1 from n.
This artificially inflates the SD (it makes it
bigger) because it makes the denominator
smaller.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Standard deviation is computed as the average
distance from the mean.
The larger the standard deviation, the more
spread out the values are.
Like the mean, the standard deviation is sensitive
to extreme scores.
If s = 0, then there is no variability among scores,
and the scores are essentially identical in value.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Variance = standard deviation squared
If you take the standard deviation and never
complete the last step (taking the square
root), you have the variation.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
While the formulas are quite similar, the two
are also quite different.
Standard deviation is stated in original units.
Variance is stated in units that are squared.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Stapelberg and colleagues looked at variability
in heart rate as it related to coronary heart
disease.
They found decreased heart rate variability in
both depressive disorders and coronary heart
disease.
Researchers think that both diseases disrupt
control feedback loops that help the heart
function efficiently.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
 Chapter 3 �Viva La Différence: Understanding Variability
 Why Understanding Variability Is Important
What You Will Learn in Chapter 3
Measures of Variability
Computing the Range
Exclusive Range
Inclusive Range
Computing Standard Deviation
Important Symbols
Why n – 1?
Things to Remember . . .
Computing Variance
Standard Deviation or Variance
Using the Computer to Compute
Understanding and Interpreting
RealWorld Stats
Chapter 4
A Picture Really Is Worth
a Thousand Words
Understanding why a picture is really
worth a thousand words
Creating a histogram and a polygon
Understanding the different shapes of
different distributions
Using SPSS to create incredibly cool charts
Creating different types of charts and
understanding their application and uses
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
A visual representation is an effective way to
examine the characteristics of a data set.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
1. Minimize the junk.
2. Plan before you start creating.
3. Say what you mean; mean what you say.
4. Label everything.
5. Communicate ONE idea.
6. Keep things balanced.
7. Maintain the scale in the graph.
8. Simple is best, and less is more.
9. Limit the number of words.
10. The chart alone should convey what you want
to say.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Method of tallying and representing the
number of times a certain score occurs
Usually group scores into interval
classes/ranges
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
A class interval is a range of numbers.
This is the first step in the creation of a
frequency distribution.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Step 1: Determine the size of each interval. Select
a class interval that has a range of 2, 5, 10, or 20
data points.
Step 2: Select a class interval so that 10–20 of the
class intervals cover the entire range of data.
Step 3: Begin listing the class interval with a
multiple of that interval.
Step 4: The largest interval goes at the top of the
frequency distribution.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Frequency Distribution
Step 1. Determine the range.
Step 2. Decide on the number of class
intervals.
Step 3. Decide on the size of the class
interval.
Step 4. Decide the starting point for the first
class.
Step 5. Create the class intervals.
Step 6. Put the data into the class intervals.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Step 1: Using a piece of graph paper, place
values at equal distance along the xaxis.
Now identify the midpoint of the class
intervals.
Step 2: Draw a bar or column around each
midpoint that represents the entire class
interval, and make its height represent the
frequency of that class interval.
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Class Intervals
Along the xAxis
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
A continuous line
that represents the
frequencies of
scores within a class
interval
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Column
Chart
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Pie Chart
Creating Histogram Graphs
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Using the Computer to Illustrate Data
Creating Bar Graphs
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Using the Computer to Illustrate Data
Creating Line Graphs
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
Using the Computer to Illustrate Data
Creating Pie Graphs
Salkind, Statistics for People Who (Think They) Hate Statistics Excel 2016 6th Edition, SAGE Inc. © 2017
 Chapter 4 �A Picture Really Is Worth a Thousand Words�
What You Will Learn in Chapter 4
Why Illustrate Data?
Ten Ways to a Great Figure
Creating a Frequency Distribution
The Classiest of Intervals
Creating Class Intervals
Frequency Distribution
Frequency Distribution Example
Creating a Histogram
Histograms
HandDrawn Histogram
TallyHo Method
Frequency Polygon
Cumulative Frequency Distribution
Other Cool Charts
Line Chart
Cool Ways to Chart Data
Using the Computer to Illustrate Data
Using the Computer to Illustrate Data
Using the Computer to Illustrate Data
Using the Computer to Illustrate Data
Chapter 7
Hypotheticals and You:
Testing Your Questions
Understanding the difference between a
sample and a population
Understanding the importance of the null
and research hypotheses
Using criteria to judge a good hypothesis
Salkind, Statistics for People Who (Think They) Hate Statistics 6th Edition, SAGE Inc. © 2017
An “educated guess”
Its role is to reflect the general problem
statement or question that is driving the
research.
Translates the problem or research question
into a form that can be tested
Salkind, Statistics for People Who (Think They) Hate Statistics 6th Edition, SAGE Inc. © 2017
Population: The large group to which you
would like to generalize your findings
Sample: The smaller, representative group of
the population that is used to do the
research
Sampling error: A measure of how well a
sample represents the population
Salkind, Statistics for People Who (Think They) Hate Statistics 6th Edition, SAGE Inc. © 2017
Two major types
Research hypotheses
Null hypotheses
Null hypotheses refer to populations and are
written in Greek symbols
Research hypotheses refer to samples and
are written in Roman symbols
Salkind, Statistics for People Who (Think They) Hate Statistics 6th Edition, SAGE Inc. © 2017
Statements that contain two or more things
that are equal (unrelated) to one another
H0 : µ1 = µ2
The starting point—it is accepted as true
without knowing more information
Benchmark to compare actual outcomes
Salkind, Statistics for People Who (Think They) Hate Statistics 6th Edition, SAGE Inc. © 2017
Statement that there is a relationship
between two variables
Statements of inequality
There are two types of research hypotheses
Directional
Nondirectional
Salkind, Statistics for People Who (Think They) Hate Statistics 6th Edition, SAGE Inc. © 2017
Reflect a difference, but the direction is not
specified
Use a twotailed test
Salkind, Statistics for People Who (Think They) Hate Statistics 6th Edition, SAGE Inc. © 2017
Nondirectional Research Hypothesis
Example
H1 : X1 ≠ X2
Salkind, Statistics for People Who (Think They) Hate Statistics 6th Edition, SAGE Inc. © 2017
Reflect a difference, and the direction is
specified
Use the onetailed test
Salkind, Statistics for People Who (Think They) Hate Statistics 6th Edition, SAGE Inc. © 2017
Differences Between Null and
Research Hypotheses
Salkind, Statistics for People Who (Think They) Hate Statistics 6th Edition, SAGE Inc. © 2017
In sum, good hypotheses should
be stated in declarative form,
posit a relationship between variables,
reflect a theory or a body of literature on
which they are based,
be brief and to the point, and
be testable.
Salkind, Statistics for People Who (Think They) Hate Statistics 6th Edition, SAGE Inc. © 2017
 Chapter 7 �Hypotheticals and You: Testing Your Questions�
 Nondirectional Research Hypothesis Example
 Differences Between Null and Research Hypotheses
What You Will Learn in Chapter 7
What Is a Hypothesis?
Samples and Populations
Hypotheses
The Null Hypothesis
The Research Hypothesis
Nondirectional Research Hypotheses
Directional Research Hypotheses
What Makes a Good Hypothesis?
DESCRIPTIVE A
N
ALYSIS
1
DESCRIPTIVE A
NA
LYSIS
8
Examining Measurements of Central Tendencies
Examining Measurements of Central Tendencies
This discussion board is based on the measurement of central tendencies whereas the nominal, ordinal, interval and ratio allow researcher to analyze data. Each of these measurements provide researchers with the ability to measure sets of data that do not represent numerical values. Salkind (201
7
) defined a level measurement with an outcome that fit into one and only class or category as nominal. The level of measurement assigns value to a specific item than assign a value to the item based on the appeal to an individual. The nominal measurement that I chose was labor force status. The descriptive characteristics that were chosen for the completion of the data set were represented some form of employment. Salkind (2017) explained the ordinal measurement as the characteristic of the assigning order or ranking data. The ordinal measurement that I chose was a ranking of how individuals view their political affiliations. The characteristics were assigned a value which for the mean, median and mode to be determined. The sum of a data set divide by the number data points represents the mean (Salkind, 2017). The mean for a data set may be skewed based on extreme number contained in the set of number. By focusing on the median, Salkind (2017) defined as a true midpoint of the data set that does not take in consideration extreme number. The median produces a more conclusive number that is related to the true data without influences. When analyzing data, situations may occur where the data is repetitive. This repetition of the number in a data is known as the mode (Salkind, 2017). A data set may have multiple modes and may have greater determining factor mean and how the data is interpreted.
Nominal Data
The nominal data set for ‘Labor for status’ comprised of
10
descriptive terms that represents some phase of employment. The data were assigned numbers 0 to 9 based on the stage of employed (e.g. “working fulltime” =1). The data set consisted of
575
respondents of which only one data was missing. The data shows that nearly
6
0% of respondents reported that were “working fulltime”. The corresponding value associated with “working fulltime” was 1. The data show that most respondents are employed in some fashion calculating a mean of
2.57
, median of 1 and a mode of 1. The median of 1 seems to be an anomaly in the data based on the data set range of nine. The standard deviation of
2
.2
4
6
and variance of
5.044
. Based on the information analyzed, 68% of the respondents are represented between .33 and 4.81. The variance shows the consistency of the data based on the distance from .33 to 4.81.
1
1
Statistic s 

Labor force status 

N 
Valid 
574 

Missing 
1  
Mean 
2.57  
Std. Error of Mean 
.094 

Median 
1.00 

Mode 

Std. Deviation 
2.246  
Variance 
5.044  
Skewness 
1.088 

Std. Error of Skewness 
.102 

Kurtosis 
.392 

Std. Error of Kurtosis 
.204 

Range 
7  
Minimum 

Maximum 
8 
57.1
12.2
8
1.4
3.3
11.1
2.8
10.3
1.7
574
100.0
Missing1
Total100.0
Labor force status 

Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Working full time 
3 28 
57.0 
57.1 

Working parttime 
70 
12.2 
69.3 

Temporarily not working 
1.4 
70.7 

Unemployed, laid off 
19 
3.3 
74.0 

Retired 
64 
11.1 
85.2 

School 
16 
2.8 
88.0 

Keeping house 
59 
10.3 
98.3 

Other 
10 
1.7 
100.0 

Total 
99.8 

NA  .2  
575 
Ordinal Data
The ordinal data set for ‘Think of self as liberal or conservative’ comprised of 10 descriptive terms that represents some political affiliation. The data were assigned numbers, or range of 0 to 9 based on what extent they represented the political affiliation (e.g. “extremely liberals” =1). The data set consisted of 575 respondents of which 28 data was missing. The data shows that nearly 72% of respondents reported that were “
Moderate
”. The corresponding value associated with “Moderate” was 4. This data set shows that individuals political affiliation lies down the middle as displayed with a mean of
3.7
7
, median of 4 and a mode of 4. The standard deviation of
1.411
and variance of
1.990
. The standard deviation shows that 68% of the individuals are willing to accept other individuals’ point of view by identifying as slightly affiliated with the political mindset.
Statistics
NValid
Missing
Mean Std. Error of Mean Median Mode Std. Deviation Variance Skewness Std. Error of Skewness Kurtosis Std. Error of Kurtosis RangeMinimum
1
Maximum7
Think of self as liberal or conservative 

547 

28  
3.
77 

.060 

4.00 

4  
1.411  
1.990  
.037 

.104 

.672 

.209 

6  
Frequency
Percent
Valid Percent
Cumulative Percent
Valid3.8
77
13.4
14.1
8
1.4
100.0
Total
547
100.0
Missing28
Total
575
100.0
Think of self as liberal or conservative 

Extremely liberal 
21 
3.7 
3.8 

Liberal 
109 
19.0 
19.9 
23.8 

Slightly liberal 
77 
13.4 
14.1 
37.8 

Moderate 
189 
32.9 
34.6 
72.4 

Slightly conservative 
86.5 

Conservative 
66 
1 1.5 
12.1 
98.5 

Extremely conservative 
1.5  
95.1 

DK 
4.9 
Interval Data
The scale data set for ‘Respondent ID’ comprised of individual that receive a number as they respondent to the survey. The data were assigned numbers, or range of 1 to 575 based on when they competed and submitted the information. The data set consisted of 575 respondents of which 1 data was missing. The data provide few measurements of central tendencies due the fact that there is no repetition and the data are straightforward.
N
Range
Minimum
Maximum
Mean
Std. Deviation
Skewness
Kurtosis
Statistic
Statistic
Statistic
Statistic
Statistic
Statistic
Statistic
Std. Error
575
574
1
575
.102
575
Descriptive Statistics 

Statistic  Std. Error  
Respondent id number 
288.00 
166.132 
.000 
1.200 
.203 

Valid N (listwise) 
References
Salkind, N. J. (2017). Statistics for people who (think they) hate statistics (Sixth edition.).
Thousand Oaks, CA: SAGE Publications, Inc.