Transcript Chapter 20 - Exploring Marketing Research

MR2300:
MARKETING RESEARCH
PAUL TILLEY
Unit 10:
Basic Data Analysis
IN THIS VIDEO:

1. Describe the various methods for summarizing data

2. Explain cross tabulations and descriptive statistics
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3. Describe the procedures for testing hypotheses
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4. Use hypothesis tests
DATA ANALYSIS
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Using Data to Answer Research Questions

Marketing researchers edit and code data to provide input that will result in
tabulated information for answering the research questions.
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With this input, researchers statistically describe project results. Once the data is
collected it is important to be able to manipulate it so as analyze its significance.

In this unit we discuss key data analysis techniques including tabulations, ordering,
graphing and hypothesis testing. As well you will have the opportunity to use
spreadsheet software to effectively analyze and present information to decision
makers.
DESCRIPTIVE ANALYSIS

The transformation of raw data into a form that will make them
easy to understand and interpret; rearranging, ordering, and
manipulating data to generate descriptive information
SUMMARIZING DATA

In order to present results in a easily understood, meaningful
format it is important to have an understanding of the basic tools
of data analysis and presentation. What follows is a summary of
the basic tools.
CENTRAL TENDENCIES
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There are three ways to measure the central tendency, and
each has a different meaning.
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mean: The mean is simply the arithmetic average. That is, the sum of
all the observations divided by the number of observations. Often
we will not have enough data to calculate the population mean, so
we will calculate a sample mean.
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median: The median is the midpoint of the distribution, or the 50th
percentile. In other words, the median is the value below which half
the values in the sample fall. To calculate the median value, simply
arrange the data from highest to lowest number, and locate/select
the middle value in the list.
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mode: The mode is the measure of central tendency that merely
identifies the value that occurs most often.
EXAMPLE: MEAN:
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22 students are asked to report the number of children that live in
their house (including brothers and sisters temporarily away at
college). The data are recorded below:
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1, 3, 4, 3, 1, 2, 2, 2, 1, 2, 2, 3, 4, 5, 1, 2, 3, 2, 1, 2, 3, 6
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There are 22 students in this class, and the total number of
children in all of their houses is 55, so the mean of this data is
55/22 , or 2.5 children per house.
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EXAMPLE: MEDIAN:

22 students are asked to report the number of children that live in
their house (including brothers and sisters temporarily away at
college). The data are recorded below:

1, 3, 4, 3, 1, 2, 2, 2, 1, 2, 2, 3, 4, 5, 1, 2, 3, 2, 1, 2, 3, 6

The median is simply the middle number in an ordered set of
data. To determine the Median, first sort the numbers into a list
and the divide the list into two equal parts. The middle value is
the Median.
1
1
1
1
1
2
2
2
2
2
2
Median is MIDDLE VALUE = 2
2
2
3
3
3
3
3
4
4
5
6
1
EXAMPLE: MODE:
1
1
1
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
22 students are asked to report the number of children that live in
their house (including brothers and sisters temporarily away at
college). The data are recorded below:
1, 3, 4, 3, 1, 2, 2, 2, 1, 2, 2, 3, 4, 5, 1, 2, 3, 2, 1, 2, 3, 6
1
2
2
2
2
2
2
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The Mode is simply the most often occurring value in the set of
data. Sort the data and then count the groups of numbers. The
biggest group is the MODE
2
2
3
3
3
3
3
4
4
5
6
2 is the
Mode Most
Often
occurring
number
TABULATION
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Tabulation - Orderly arrangement of data in a table or other
summary format
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Frequency table
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Percentages
FREQUENCY TABLE & CHART
Frequency
Data
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The arrangement of statistical data in
a row-and-column format that exhibits
the count of responses or observations
for each category assigned to a
variable
Frequency
8
7
6
5
4
3
2
1
# of Children per household
1
1
1
1
1
2
2
2
2
2
2
2
2
3
3
3
3
3
4
4
5
6
5
8
5
2
1
1
Frequency
PERCENTAGES
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Whether the data are tabulated by
computer or by hand, percentages,
cumulative percentages, and
frequency distributions are useful.
1
1
1
1
1
2
2
2
2
2
2
2
2
3
3
3
3
3
4
4
5
6
Percentages
5
23%
8
36%
5
23%
2
9%
1
1
5%
5%
TABULATION
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Tabulation refers to the orderly arrangement of data in a table or other summary
format.
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Counting the number of responses to a question and putting them into a frequency
distribution is a simple tabulation, or marginal tabulation, which provides the most
basic form of information for the researcher.
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A frequency table is the arrangement of statistical data in a row and column format
that exhibits the count of responses or observations for each of the categories or
codes assigned to a variable.
CROSS-TABULATION
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A technique for organizing data by groups, categories, or classes,
thus facilitating comparisons; a joint frequency distribution of
observations on two or more sets of variables
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Contingency table- The results of a cross-tabulation of two
variables, such as survey questions
CROSS-TABULATION
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Example:
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Question 1: Are you Male Female
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Question 2: Do you buy Diet Coke
RESULTS
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22Summary
Male or Female M M M M M M M F F F F F F F F F F F F F F F 7 Males / 15 Females
Buy Diet Coke
Yes No No No No No No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No No No No No 11 Yes / 11 No
Initial analysis:
N=22
Males = 7
Females = 15
½ of those surveyed buy Diet Coke
CROSS-TABULATION
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Example:
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Question 1: Are you Male Female
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Question 2: Do you buy Diet Coke
RESULTS
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22Summary
Male or Female M M M M M M M F F F F F F F F F F F F F F F 7 Males / 15 Females
Buy Diet Coke
Yes No No No No No No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No No No No No 11 Yes / 11 No
Cross tabulation indicates:
1 in 7 males (in our study) buy Diet Coke (only 15% of males buy Diet Coke)
10 in 15 females buy Diet Coke (68% of females buy Diet Coke)
CONCLUSION: Diet Coke is mostly purchased by Females
DATA TRANSFORMATION
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Data conversion
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Changing the original form of the data to a new format
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More appropriate data analysis
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New variables
COLLAPSING A FIVE-POINT SCALE
Data Transformation example
 Strongly
Agree
10
 Agree
10
 Neither
5
Agree nor
Disagree
 Disagree
20
 Strongly
15
Disagree
 Strongly
Agree/Agree 20
 Neither
Agree nor
Disagree
 Disagree/Strongly
Disagree
5
35
INDEX NUMBERS
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Score or observation recalibrated to indicate how it relates to a
base number
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CPI - Consumer Price Index
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Current item price ($) = (base year price) * (Current CPI) / (Base
year CPI)
EXCEL FOR ANALYSIS
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Microsoft Excel is a popular spreadsheet package that allows for
entering and editing data with minimal effort. It also incorporates
descriptive analysis, graphic analysis, and limited statistical
analysis.
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In Excel statistical calculations can be performed using the Data
Analysis and Paste Function menus.
HYPOTHESIS TESTING & ERROR
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The process of hypothesis testing goes as follows:
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1) Determine a statistical hypothesis.
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2) Take an actual sample and calculate the sample mean. (The sample value should
reflect the true value of the population parameter. The difference between the sample
mean and the actual population mean is called the Error)
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3) Determine if the deviation between the obtained value of the sample mean and its
expected value would have occurred by chance alone—that is, if the statistical
hypothesis is true. If the sample mean is significantly different – reject the null hypotheses
The researcher runs the risk of committing two types of errors. A Type I error, which has the
probability alpha —the level of significance that we have set up—is an error caused by
the rejection of the null hypothesis when it is true.
A Type II error has the probability of beta and it is an error caused by the failure to reject
the null hypothesis when the alternative hypothesis is true.