2. PURPOSE OF
ANALYSING THE DATA
- Learn the problem
- Find out the cause and the effect of the
phenomena
- Predict real phenomena based on research
- Find out answer of various problem
- Draw conclusion based on the problem
BASIC ELEMENTS IN ANALYSING THE DATA
- What (data/information)
- Who/where/how/what happen
(Scientific reasoning/argument)
- What result (Finding)
- So what/so how/therefore (Lesson/conclusion)
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4. CHOOSE BASED ON CHARACTERISTICS OF
THE DATA
QUALITATIVE
QUANTITATIVE
EXAMPLES
- Quality of life of the local
community in Ubud
- Comparative analysis of
students’ achievement
between girls and boys in
- Local perception of tourism as
tourism institute
an indicator of destination
decline
- The effect of increase fuel
price towards local tourist
arrival
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6. QUALITATIVE
Miles and Huberman (1994), analysis of qualitative data is NOT
sequential steps but happen at the same time plus over and over again.
Data collection
Data display
Data reduction
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Drawing /
verifying
conclusion
7. A process of ...
• Data collection collecting & gathering the data
in a form of a list easier to be read and
analyzed
• Data reduction transforming, selecting,
adding or reducing based on the needs
• Data display classifying, categorizing, put the
data in which share certain similarities
• Concluding verifying & formulating the
conclusion that can answer the phenomena
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8. QUALITATIVE
Model by James P. Spradley
Pengamatan deskriptif
Pengamatan terfokus
Pengamatan terpilih
Component analysis
Taxonomy analysis
Domain analysis
Beginning of the research
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End of the research
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9. DOMAIN taxonomy component ...
• finding out the description as a whole about the problem being
analyzed
• description the universal semantic relationship (9 types)
• Example:
No Semantic Relationship Sample Forms
1
X adalah jenis dari Y
2
Area/Ruang
X adalah bagian dari Y
3
Cause-effect/
Sebab-akibat
X adalah sebab dari Y
4
Reason/alasan
X adalah alasan melakukan Y
5
Location/Lokasi
X adalah tempat melakukan Y
6
The way to/Cara
X adalah cara melakukan Y
7
Function/Fungsi
X digunakan untuk mencari Y
8
Sequence/Urutan
X adalah urutan dalam proses Y
9
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Form/Jenis
Characteristic/
karakteristik
X adalah karakteristik dari Y
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10. Listing domain based on the fact
formulate question for each domain
• Mahasiswa asing (pertukaran mhs Indonesia - Belanda)
• Domain yg berkaitan dg jenis: (studi yang diambil, kegiatan seharihari, pengeluaran sehari-hari)
• Domain yg berkaitan dg ruang: (tempat tinggal, jarak dari kampus,
lingkungan tempat tinggal)
• Domain yg berkaitan dg sebab-akibat: (sebab mengikuti pertukaran
mahasiswa, sebab memilih studi ini, sebab memilih Indonesia)
• Domain yg berkaitan dg alasan: (alasan jalan kaki ke kampus,
alasan menyewa kos-kosan dengan harga tsb, alasan berbelanja ke
pasar)
• Domain ...
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11. domain TAXONOMY component ...
• Deeper analysis on certain domain based on the
needs/research focus
• Only use domains which have relationship with the
research being analysed
• Organizing elements with sharing the similarity in a
domain
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12. domain TAXONOMY component ...
• Example: tourists guide’s licence
- Domain function function of guiding licence for tourist guide
1. individual’s identity
1.1 lifelihood
1.2 legal prefession
2. association’s identity
2.1 members of association community
2.2 working channel
3. working access
3.1 enter all destinations easily
3.2 guiding in all destinations
4. credibility
4.1 confidence in guiding
4.2 giving trust to tourist
4.3 giving safety and security
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13. domain TAXONOMY component ...
Guiding licence
function
Association
identity
Individual
identity
lifelihood
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Enter all
destinations
Legal
profession
Members
community
Working
access
Working
channel
Credibility
Guiding in
all places
Confidence
in guiding
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Giving trust
to tourist
Giving
safety
and
security
14. domain taxonomy COMPONENT ...
• Contrasting the elements in a domain through
observation, interview, ...
• Example:
Working access
credibility
...
Individual
identity
Able to enter all
destinations easily
Confidence in
guiding tourist
...
Association
identity
Provide / sharing
more channels /
means for
promotion
giving value for
guiding profession
...
...
...
...
...
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15. domain taxonomy component THEMES
• Correlating all domains from different point of view, e.g.
values, symbols, habitual, tradition,...
• Discovering cultural themes
• How to do:
- Deeply involved in research domain (paricipant observation)
- Identifying and organizing the domains
- Contrasting all domains including their elements (enriching
content)
- Finding the similarities and differences among the domains
and making correlation
- Finding supportive or contrastive literatures and theory (if
any) to compare and/or to test
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16. Qualitative data analysis,
in short ...
• Make list
• Organize into certain pattern
• Interpretate data (explain distribution + pattern +
relation + deep meaning)
• While analysing, compare it to literature/theoretical
review to confirm the theory / to invent new theory
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17. Additional info for qualitative
data analysis
• New research no literature study to compare how to check the
validity of the data?
• Since some say that the foundation of qualitative are words
structured...to avoid this misconception, use triangulation!
• Findings of a study are true and certain—“true” means accurately
reflect the situation, and “certain” means supported by evidence.
1. Data triangulation (using variety of data source)
2. Investigator triangulation (using several investigator/team)
3. Theory triangulation (using multiple theory from different
discipline to interpretate single data)
4. Methodological triangulation (using multiple method to study a
single problem,e.g. FGD, survey, interview)
(Denzin, 1978)
5. Environmental triangulation (using different location, setting,
others related to environment); as long as the finding remain the
same although it’s influenced by environment factor validity is
established.
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19. DESCRIPTIVE STATISTICS
Data distribution form
– Mean
– Median
– Modus
– Standar deviasi, range, koefisien variasi
Data display
– Tabel
– Gambar/grafik
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20. DESCRIPTIVE STATISTICS
• Analyzing data by describing the collected data with no
means to generalize
• Data are gathered from population
• In such case, it can be gathered from sample, but please
NOTE that the result cannot represent the population
• Example:
• Of 350 randomly students in SPB, 280 students had
choosen food production course. An example of
descriptive statistics is the following statement : "80% of
these students had choosen food production course."
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21. INFERENTIAL STATISTICS
• Analyzing data by using information from a sample to
infer something about a population
• The result can be used to generalize
• Example:
• Of 350 randomly students in SPB, 280 people had
choosen food production course. An example of
inferential statistics is the following statement : "80% of
SPB students had choosen food production course."
• The easiest way to tell that this statement is not
descriptive is by trying to verify it based upon the
information provided and or hypothesis testing
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22. INFERENTIAL STATISTICS
• a result is considered significant not because it is
important or meaningful, but because it has been
predicted as unlikely to have occurred by chance alone.
• Level of significance is usually at 0.05 (5%)
• be less than 0.05, then the result would be considered
statistically significant and the null hypothesis would be
rejected.
• Example: ...
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23. INFERENTIAL STATISTICS
• Example: ...level of significance
•
•
•
•
probably no difference between city and the suburbs, the probability is .795
(1 - 0.795 = 0.205) only a 20.5% chance that the difference is true.
In contrast the high significance level for type of vehicle 0.001
(1 – 0.001 = 0.999) 99.9% indicates there is almost certainly a true
difference in purchases of Brand X by owners of different vehicles in the
population from which the sample was drawn.
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24. Parametric Analysis
• Data scale interval/ratio
• Normal distribution
Example:
Comparative analysis
Independent t test, paired t test, Analysis Of Variances
(ANOVA), Analysis Of Covariance (ANCOVA)
Corelation Analysis
Corelation Product Moment, Corelation Partial, Analysis
Regression
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25. Non-Parametric Analysis
Non-parametric Analysis
• Data scale nominal/ordinal
• Data scale interval/ratio with NO normal distribution
Example
Comparative analysis
• Chi square, Kolmogorov Smirnov, Mann-Whitney,
Wilcoxon, Kruskall Wallis, Friedman
Corelation Analysis
• Corelation Rank Spearman, Tau Kendall, Coefficient
Contingency, Gamma
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26. Data in Tourism Study
• Many tourism researches are in qualitative analysis
• Qualitative quantify the data Quantitative
scale
• Research Example:
• “Tourist satisfaction level toward workers’ service
quality in hotel X”
• “Staffs’ knowledge about environment hygiene and
sanitation in restaurant XX”
• “Workers’ attitude toward the manager’s leadership
style in hotel XXX”
• Receptionists’ skill in selling hotel room to the
customer in hotel XXXX
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27. Ordered Response Option
in Likert Scale
Indicator
1
2
3
4
5
Satisfaction
Not at all
satisfied
Slightly satisfied
Somewhat
satisfied
Very
satisfied
Extremely
satisfied
Attitude
Strongly
Disagree
Disagree
Neither Agree
nor Disagree
Agree
Strongly Agree
Knowledge
Very Poor
Poor
Fair
Good
Very Good
Skill
Very Poor
Poor
Fair
Good
Very Good
Education
Much Lower
Slightly Lower
About the Same
Higher
Much Higher
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28. Questionnaire of
Tourist/customer Satisfaction
• Satisfaction Indicators by Parasuraman
Satisfaction Indicators
1
Tangible:
- Hotel facilities
- ...
Reliability:
- Value of the product
- ...
Responsiveness:
- ...
- ...
Assurance:
- ...
- ...
Empathy:
- ...
- ...
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2
3
4
5
29. “Guest satisfaction level toward hotel
workers’ service quality in Sanur area”
• 5 hotels
• 50 respondents each
Descriptive
Inferential
Find out score of each indicator to
describe the variable condition
without testing / corelating /
without comparing
Comparing two variables (or more)
and measuring their relationship by
applying set of test
No generalizing
Generalizing
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30. Score
Satisfaction level
Total
%
Total Score
1
Not at all satisfied
78
31
78
2
Slightly satisfied
63
25
126
3
Somewhat satisfied
67
27
201
4
Very satisfied
24
10
96
5
Extremely satisfied
18
7
90
Total
250
100
591
Average
2.4
Based on the data above, 31% indicates the guests are not at
all satisfied, 25% the guests are slightly satisfied, ...
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31. • (Class Interval )
Ci = range
K
Ci = (5-1) = 0.8
5
Score
Category
Score interval
% interval
1
Not at all satisfied
1.0 -< 1.8
20 -< 36
2
Slightly satisfied
1.8 -< 2.6
36 -< 52
3
Somewhat satisfied
2.6 -< 3.4
52 -< 68
4
Very satisfied
3.4 -< 4.2
68 -< 84
5
Extremely satisfied
4.2 -< 5.0
84 -< 100
Based on the average score of 2.4 , the score interval category of guest
satisfaction level is slightly satisfied. The hotel manajemen should
improve their service quality.
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