Applied Mixed Research to Business: Washington Post Facebook Analysis

This report employs a mixed research approach, combining psychographic segmentation and cluster analysis, to classify and analyze comments on the Washington Post's Facebook posts. The study aims to identify distinct segments based on the opinions expressed in the comments, using a coding scheme with nine variables. Limitations include the potential for sarcasm in comments and the challenges of ensuring coding consistency. The analysis identifies three segments: Conservatives, Future-leaning and Emulators, and Free Thinkers and Independents, each characterized by their views on clarity, emotionality, focus, and criticism of government. The report concludes by recommending specific products and services tailored to each segment, such as books on religion and history for Conservatives, technology gadgets for Future-leaning and Emulators, and books on philosophy for Free Thinkers and Independents. The appendices include SPSS cluster outputs and the Excel data file used for the analysis. Desklib provides students access to similar solved assignments and resources.

APPLIED MIXED RESARCH TO BUSINESS
Objectives
The objectives of this study include:
1. To classify the comments on the Washington Post Facebook posts by using clustering.
2. To develop segments for the clusters of comments on the Washington Post Facebook
posts.
3. To recommend products and/or services for the various segments.
Method
This study applies a mixed research approach where two research methods are used together to
produce the conclusions to the study. The first method applied is the psychographic
segmentation. This research method can be defined as the process of analyzing consumer data
with the aim of getting a deeper understanding of the consumer beyond the purchases made
(Kennedy and Weinstein 2010). The psychographic segmentation aims at understanding the
interest, activities and opinions of the consumer or prospective consumer. In this study, focus
will be on gathering the opinions from the Facebook Accounts that commented on the various
posts published by the Washington Post on their Facebook page.
The second research method that will be used together with the psychographic segmentation is
the clustering method (or cluster analysis). Cluster Analysis is an exploratory data analysis tool
that classifies objects in a set depending on predetermined or existing characteristics (Galit, et al.
2018). In the predetermined case, a researcher can select the characteristics. In the existing case,
the algorithm used for the analysis identifies the characteristic and then classifies the objects
accordingly. For this study, the objects being classified will be the comments on the posts by the
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APPLIED MIXED RESARCH TO BUSINESS
Washington Post on their Facebook page. The characteristics will be the nine variables in the
provided coding scheme.
Limitations
The limitations in this study include the following:
1. Some of the comments may be sarcastic. This presents a challenge in the coding, since
the opinions presented in the comments may not be representative of the true opinion of
the individual.
2. The coding processes may be tedious especially in cases where the sample size is large.
In such cases, a lot of care should be taken to ensure that proper coding is done in order
to guarantee consistency in the research process.
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APPLIED MIXED RESARCH TO BUSINESS
Segments Developed
The resultant output from the SPSS Analysis is given in the tables below:
Table 1: Final Cluster Centers
Final Cluster Centers
Cluster
1 2 3
Q1 4 4 4
Q2 5 4 3
Q3 3 4 4
Q4 3 5 5
Q5 3 5 5
Q6 5 3 5
Q7 4 2 6
Q8 4 4 4
Q9 4 4 5
Table 2: Number of Cases in Each Cluster
Number of Cases in each
Cluster
Cluster 1 69.000
2 65.000
3 66.000
Valid 200.000
Missing .000
From the results in Table 1: Final Cluster Centers the following segments were developed:
1. Conservatives
This segments makes up cluster 1 in Table 1: Final Cluster Centers. This cluster can be
profiled as follows:
a. Fairly clear on ideas and reasonably objective.
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APPLIED MIXED RESARCH TO BUSINESS
b. Relatively emotional about issues.
c. Focused on the past and present.
d. Averagely critical of the government and cooperates.
2. Future-leaning and Emulators
This segments makes up cluster 2 in Table 1: Final Cluster Centers. This cluster can be
profiled as follows:
a. Fairly clear on ideas and averagely objective.
b. Averagely emotional about issues.
c. Focused on the future.
d. Highly focused on personalities.
e. Averagely critical of the government and cooperates.
3. Free Thinkers and Independents
This segments makes up cluster 3 in Table 1: Final Cluster Centers. This cluster can be
profiled as follows:
a. Fairly clear on ideas and averagely objective.
b. Not emotional about issues.
c. Not focused on personalities.
d. Averagely Critical of the government and cooperates.
Products and Services Recommended for Segments.
The table below outlines the possible products and/or services that can be recommended for the
various segments:
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APPLIED MIXED RESARCH TO BUSINESS
Table 3: Product and Services Recommended for Segments
Segment Recommended Product and/or Service
I. Conservatives i. Books on Religion and History
ii. Products and services from
established/traditional/mainstream
brands
II. Future-leaning and Emulators i. Clothes and Clothing Accessories
ii. Latest Technology Gadgets
iii. Products and Services branded as
green solutions.
III. Free Thinkers and Independents i. Books on Philosophy and Ideologies
ii. Educational Courses
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APPLIED MIXED RESARCH TO BUSINESS
Appendix A: SPSS CLUSTER OUTPUT
GET DATA /TYPE=XLSX
/FILE='C:\Users\user\Documents\FBCode.xlsx'
/SHEET=name 'FBCode'
/CELLRANGE=full
/READNAMES=on
/ASSUMEDSTRWIDTH=32767.
EXECUTE.
DATASET NAME DataSet1 WINDOW=FRONT.
QUICK CLUSTER Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9
/MISSING=LISTWISE
/CRITERIA=CLUSTER(3) MXITER(10) CONVERGE(0)
/METHOD=KMEANS(NOUPDATE)
/PRINT INITIAL.
Final Cluster Centers
Cluster
1 2 3
Q1 4 4 4
Q2 5 4 3
Q3 3 4 4
Q4 3 5 5
Q5 3 5 5
Q6 5 3 5
Q7 4 2 6
Q8 4 4 4
Q9 4 4 5
Number of Cases in each
Cluster
Cluster
1 69.000
2 65.000
3 66.000
Valid 200.000
Missing .000
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APPLIED MIXED RESARCH TO BUSINESS
Appendix B: Excel Data File
COMMEN
T
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9
1 7 2 4 2 4 1 7 6 2
2 5 4 4 1 6 5 6 2 7
3 7 7 3 6 7 1 2 6 1
4 2 2 6 6 5 7 4 1 7
5 7 4 2 1 4 6 3 6 2
6 6 3 7 5 7 6 1 1 2
7 4 5 1 1 2 7 1 7 2
8 5 5 4 6 5 2 4 7 2
9 5 3 6 6 1 1 3 1 4
10 4 3 3 4 7 6 3 7 7
11 2 4 3 3 4 7 6 7 1
12 6 6 2 1 7 5 2 1 6
13 6 1 2 2 6 2 3 7 6
14 6 7 7 2 1 5 2 7 5
15 3 1 2 7 7 3 7 3 3
16 3 3 3 1 6 4 7 6 2
17 6 4 2 5 1 6 4 3 7
18 2 3 1 4 2 4 3 1 2
19 2 5 4 1 2 3 7 3 2
20 4 1 3 1 7 7 4 4 6
21 7 4 7 2 4 4 1 4 4
22 6 6 2 3 1 1 3 4 5
23 5 2 1 7 5 6 5 1 2
24 7 6 6 2 1 4 7 7 2
25 4 5 5 3 4 1 1 3 6
26 3 7 5 7 6 7 7 3 5
27 2 5 2 1 2 5 3 3 5
28 2 1 4 7 5 5 1 2 6
29 1 1 7 1 5 1 2 6 4
30 5 7 3 5 1 4 6 2 6
31 3 1 2 7 6 2 4 6 6
32 4 4 2 1 7 2 1 1 6
33 1 7 4 2 7 5 7 4 3
34 3 7 2 2 1 5 1 5 6
35 3 7 2 7 4 4 7 2 2
36 2 7 7 3 2 2 7 3 4
37 4 3 7 1 2 4 1 5 3
38 4 7 2 6 7 4 4 5 7
39 5 1 1 3 7 7 7 2 5
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APPLIED MIXED RESARCH TO BUSINESS
40 4 5 1 7 7 6 2 7 4
41 3 4 2 6 7 4 2 5 5
42 2 7 7 2 1 5 5 3 7
43 6 7 6 3 5 1 6 1 4
44 4 5 6 3 3 3 3 6 2
45 5 4 6 7 6 6 5 6 5
46 6 7 6 1 1 7 4 3 7
47 3 6 2 6 7 3 2 4 3
48 1 6 1 6 7 2 1 6 2
49 6 2 6 2 1 1 7 3 7
50 7 3 1 1 1 5 5 2 4
51 4 7 2 4 4 6 6 1 5
52 3 4 1 7 6 4 7 5 4
53 5 2 2 7 2 2 6 4 1
54 6 6 4 5 1 1 7 4 1
55 7 6 1 7 7 1 6 5 3
56 4 7 7 2 1 7 6 4 4
57 3 2 6 6 6 5 7 4 4
58 2 6 5 5 5 3 4 2 5
59 4 1 3 7 6 6 2 1 6
60 6 2 6 5 4 4 1 1 2
61 6 5 2 1 6 7 3 1 6
62 1 2 7 2 6 3 5 7 5
63 5 3 4 4 6 2 3 1 1
64 5 7 6 1 1 3 5 5 1
65 7 4 4 5 5 2 1 1 1
66 5 3 4 4 7 1 6 3 5
67 1 6 1 1 3 3 1 6 7
68 3 4 2 3 7 2 7 6 5
69 4 2 7 1 2 4 2 7 7
70 4 7 7 7 7 5 1 4 2
71 7 2 7 7 7 7 7 4 2
72 6 5 4 4 1 2 7 1 7
73 4 3 5 5 7 5 4 3 7
74 1 6 2 6 1 5 6 7 7
75 1 5 2 5 3 5 3 3 4
76 6 3 3 7 3 6 7 1 5
77 7 2 3 4 7 4 7 1 2
78 2 6 6 2 6 7 1 3 3
79 6 5 2 7 6 5 3 2 3
80 6 5 3 2 3 5 4 2 7
81 1 1 2 3 2 6 7 1 1
82 2 5 7 5 3 2 6 7 4
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APPLIED MIXED RESARCH TO BUSINESS
83 2 6 3 7 7 5 1 6 4
84 4 7 4 1 6 3 1 3 5
85 2 6 4 2 5 1 1 6 3
86 6 3 1 4 2 6 6 1 6
87 4 1 5 7 4 5 3 6 5
88 1 5 2 6 5 5 3 2 1
89 5 7 6 1 4 6 4 6 7
90 7 7 2 6 3 3 4 7 1
91 6 6 3 7 1 7 2 2 1
92 2 7 3 2 7 7 5 7 6
93 4 3 1 1 1 1 3 3 6
94 2 5 2 5 6 1 1 1 7
95 5 1 2 1 7 2 7 7 4
96 6 3 5 3 4 6 7 6 3
97 2 6 6 7 3 3 7 3 3
98 4 1 7 5 2 4 5 2 5
99 4 2 7 1 5 5 3 3 4
100 5 2 1 3 2 7 2 1 4
101 4 3 7 4 1 6 6 4 4
102 1 4 3 6 1 5 1 7 6
103 6 2 3 5 6 1 6 5 5
104 7 3 7 6 4 7 7 4 2
105 1 3 6 5 1 5 5 6 5
106 4 7 5 3 3 3 7 1 4
107 6 6 5 4 5 1 3 6 7
108 7 1 3 7 1 5 4 7 7
109 5 1 6 5 5 1 2 2 1
110 5 6 2 1 6 4 6 1 5
111 6 5 4 7 4 6 2 7 7
112 6 1 3 4 6 1 7 7 6
113 3 4 2 5 6 4 4 5 7
114 7 3 7 4 1 6 7 7 5
115 7 1 7 1 3 4 5 2 7
116 4 4 5 4 6 5 6 4 4
117 5 2 5 5 3 5 4 6 4
118 4 5 7 7 7 1 2 5 5
119 1 4 1 6 1 7 6 6 2
120 6 6 3 2 5 2 4 2 1
121 2 3 7 6 5 6 5 1 7
122 5 6 2 4 6 2 4 4 6
123 5 5 6 7 6 2 1 7 5
124 3 7 6 1 2 7 7 4 4
125 7 6 3 3 5 1 3 5 3
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APPLIED MIXED RESARCH TO BUSINESS
126 5 2 1 3 5 4 3 5 7
127 6 6 3 3 2 4 2 4 3
128 2 4 3 6 1 2 1 3 7
129 7 7 4 7 6 6 6 6 4
130 5 2 5 7 4 3 1 1 4
131 6 5 2 7 5 6 1 6 4
132 3 2 7 6 5 1 2 7 1
133 1 4 4 5 3 6 7 5 1
134 4 1 1 4 5 1 6 7 1
135 5 5 5 3 4 4 7 2 5
136 3 2 7 1 7 1 1 7 7
137 6 1 3 2 6 7 6 7 2
138 4 3 4 5 7 2 7 1 6
139 2 1 5 1 2 6 3 6 6
140 1 2 2 4 4 6 4 6 6
141 6 4 2 3 3 6 2 6 2
142 7 5 2 1 6 7 2 1 2
143 7 6 4 5 4 3 6 2 7
144 5 2 5 6 7 3 7 3 7
145 5 3 4 1 3 3 5 4 2
146 5 5 7 3 2 5 5 3 3
147 3 2 3 4 3 3 2 1 3
148 7 6 5 6 4 3 3 7 7
149 3 5 6 1 7 5 7 2 6
150 3 3 1 3 6 2 4 2 6
151 6 4 6 6 3 3 2 2 5
152 4 2 4 4 6 5 7 4 6
153 3 4 2 7 7 6 3 1 4
154 2 5 1 4 1 2 4 6 3
155 7 1 1 1 2 1 4 1 3
156 7 2 1 5 3 5 6 5 7
157 3 5 2 3 2 3 2 5 7
158 6 5 1 2 4 3 7 7 5
159 6 4 2 2 1 6 6 5 1
160 6 6 3 3 6 3 2 5 1
161 4 4 3 6 3 7 5 4 2
162 1 7 5 4 5 6 6 7 2
163 1 6 3 2 3 7 1 4 6
164 1 6 3 5 4 2 2 7 4
165 4 7 6 4 3 7 7 5 5
166 3 4 3 3 1 6 1 1 2
167 1 3 1 2 4 3 3 1 7
168 4 1 5 4 3 7 1 4 7
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APPLIED MIXED RESARCH TO BUSINESS
169 3 5 3 5 7 5 2 4 2
170 2 2 1 7 3 5 7 1 6
171 7 7 5 2 5 7 6 6 4
172 7 6 1 1 7 7 4 3 3
173 6 1 4 1 4 2 3 6 7
174 4 5 1 1 4 1 5 6 5
175 5 6 2 1 7 5 4 4 2
176 2 5 6 5 4 7 7 3 6
177 5 5 2 5 7 6 6 7 6
178 3 4 6 5 6 6 2 6 1
179 1 3 7 5 4 2 3 5 3
180 7 3 2 4 6 3 5 2 2
181 2 7 4 2 7 5 1 5 2
182 5 6 1 7 1 5 5 7 3
183 4 2 7 6 4 7 1 4 3
184 6 5 3 1 6 3 4 5 2
185 3 7 7 2 3 5 7 5 4
186 6 5 5 4 3 7 3 2 6
187 6 3 1 4 1 1 2 1 5
188 7 6 2 5 2 7 2 6 5
189 5 3 5 4 4 7 2 7 1
190 6 3 7 4 6 3 4 6 2
191 4 1 4 6 6 2 4 5 4
192 4 5 4 3 7 1 3 5 1
193 5 1 3 7 7 7 4 4 5
194 3 3 7 5 1 2 3 6 6
195 3 7 3 1 4 2 4 2 4
196 5 6 6 7 7 6 4 7 1
197 1 1 7 6 4 5 7 2 4
198 4 1 7 1 4 1 4 5 5
199 1 7 4 7 2 2 2 3 3
200 6 6 5 6 5 7 6 3 4
References
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Galit, S, Bruce C Peter, Y Inbal, N R Patel, and L C Kenneth. 2018. Data Mining for Business
Analytics. 1st. New Delhi: John Wiley & Sons, Inc.
Kennedy, M G, and A Weinstein. 2010. "Psychographic Segmentation of the Self-Employed: An
Explanatory Study." New England Journal of Entrepreneurship 1-15. 13(2).
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