0
ANALISIS DATA
SARTIKA SARI

12/12/2013
PURPOSE OF
ANALYSING THE DATA
- Learn the problem
- Find out the cause and the effect of the
phenomena
- Predict real phen...
BASIC CONCEPT

DATA ANALYSIS

Simplifying process  easily be
understood

QUALITATIVE

12/12/2013

MIXED

-sartika-

QUANT...
CHOOSE BASED ON CHARACTERISTICS OF
THE DATA
QUALITATIVE

QUANTITATIVE
EXAMPLES

- Quality of life of the local
community i...
QUALITATIVE
Circular process of qualitative analysis
DESCRIBING

CLASSIFYING

CONNECTING

Dey, (1993: 32)
12/12/2013

-sar...
QUALITATIVE
Miles and Huberman (1994), analysis of qualitative data is NOT
sequential steps but happen at the same time pl...
A process of ...
• Data collection  collecting & gathering the data
in a form of a list  easier to be read and
analyzed
...
QUALITATIVE
Model by James P. Spradley

Pengamatan deskriptif
Pengamatan terfokus
Pengamatan terpilih

Component analysis
...
DOMAIN  taxonomy  component  ...
• finding out the description as a whole about the problem being
analyzed
• descriptio...
Listing domain based on the fact 
formulate question for each domain
• Mahasiswa asing (pertukaran mhs Indonesia - Beland...
domain  TAXONOMY  component  ...
• Deeper analysis on certain domain based on the
needs/research focus
• Only use domai...
domain  TAXONOMY  component  ...
• Example: tourists guide’s licence
- Domain function  function of guiding licence fo...
domain  TAXONOMY  component  ...

Guiding licence
function

Association
identity

Individual
identity

lifelihood

12/1...
domain  taxonomy  COMPONENT  ...
• Contrasting the elements in a domain through
observation, interview, ...
• Example:
...
domain  taxonomy  component  THEMES
• Correlating all domains from different point of view, e.g.
values, symbols, habit...
Qualitative data analysis,
in short ...
• Make list
• Organize into certain pattern
• Interpretate data (explain  distrib...
Additional info for qualitative
data analysis
• New research  no literature study to compare  how to check the
validity ...
KUANTITATIF

STATISTIK DESKRIPTIF

STATISTIK INFERENSIAL

STATISTIK PARAMETRIK

12/12/2013

-sartika-

STATISTIK NONPARAME...
DESCRIPTIVE STATISTICS
Data distribution form
– Mean
– Median
– Modus
– Standar deviasi, range, koefisien variasi
Data dis...
DESCRIPTIVE STATISTICS
• Analyzing data by describing the collected data with no
means to generalize
• Data are gathered f...
INFERENTIAL STATISTICS
• Analyzing data by using information from a sample to
infer something about a population
• The res...
INFERENTIAL STATISTICS
• a result is considered significant not because it is
important or meaningful, but because it has ...
INFERENTIAL STATISTICS
• Example: ...level of significance

•
•
•
•

probably no difference between city and the suburbs, ...
Parametric Analysis
• Data scale interval/ratio
• Normal distribution

Example:
Comparative analysis
Independent t test, p...
Non-Parametric Analysis
Non-parametric Analysis
• Data scale nominal/ordinal
• Data scale interval/ratio with NO normal di...
Data in Tourism Study
• Many tourism researches are in qualitative analysis
• Qualitative  quantify the data  Quantitati...
Ordered Response Option
in Likert Scale
Indicator

1

2

3

4

5

Satisfaction

Not at all
satisfied

Slightly satisfied

...
Questionnaire of
Tourist/customer Satisfaction
• Satisfaction Indicators by Parasuraman
Satisfaction Indicators

1

Tangib...
“Guest satisfaction level toward hotel
workers’ service quality in Sanur area”
• 5 hotels
• 50 respondents each

Descripti...
Score

Satisfaction level

Total

%

Total Score

1

Not at all satisfied

78

31

78

2

Slightly satisfied

63

25

126
...
• (Class Interval )
Ci = range
K
Ci = (5-1) = 0.8
5
Score
Category

Score interval

% interval

1

Not at all satisfied

1...
Thank you
Upcoming SlideShare
Loading in...5
×

Analisis Data

3,039

Published on

Published in: Education, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
3,039
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
96
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Transcript of "Analisis Data"

  1. 1. ANALISIS DATA SARTIKA SARI 12/12/2013
  2. 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) 12/12/2013 -sartika-
  3. 3. BASIC CONCEPT DATA ANALYSIS Simplifying process  easily be understood QUALITATIVE 12/12/2013 MIXED -sartika- QUANTITATIVE
  4. 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 12/12/2013 -sartika-
  5. 5. QUALITATIVE Circular process of qualitative analysis DESCRIBING CLASSIFYING CONNECTING Dey, (1993: 32) 12/12/2013 -sartika-
  6. 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 12/12/2013 -sartika- Drawing / verifying conclusion
  7. 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 12/12/2013 -sartika-
  8. 8. QUALITATIVE Model by James P. Spradley Pengamatan deskriptif Pengamatan terfokus Pengamatan terpilih Component analysis Taxonomy analysis Domain analysis Beginning of the research 12/12/2013 End of the research -sartika-
  9. 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 12/12/2013 Form/Jenis Characteristic/ karakteristik X adalah karakteristik dari Y -sartika-
  10. 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 ... 12/12/2013 -sartika-
  11. 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 12/12/2013 -sartika-
  12. 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 12/12/2013 -sartika-
  13. 13. domain  TAXONOMY  component  ... Guiding licence function Association identity Individual identity lifelihood 12/12/2013 Enter all destinations Legal profession Members community Working access Working channel Credibility Guiding in all places Confidence in guiding -sartika- Giving trust to tourist Giving safety and security
  14. 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 ... ... ... ... ... 12/12/2013 -sartika-
  15. 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 12/12/2013 -sartika-
  16. 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 12/12/2013 -sartika-
  17. 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. -sartika12/12/2013
  18. 18. KUANTITATIF STATISTIK DESKRIPTIF STATISTIK INFERENSIAL STATISTIK PARAMETRIK 12/12/2013 -sartika- STATISTIK NONPARAMETRIK
  19. 19. DESCRIPTIVE STATISTICS Data distribution form – Mean – Median – Modus – Standar deviasi, range, koefisien variasi Data display – Tabel – Gambar/grafik 12/12/2013 -sartika-
  20. 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." 12/12/2013 -sartika-
  21. 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 -sartika12/12/2013
  22. 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: ... -sartika12/12/2013
  23. 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. 12/12/2013 -sartika-
  24. 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 12/12/2013 -sartika-
  25. 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 12/12/2013 -sartika-
  26. 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 12/12/2013 -sartika-
  27. 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 12/12/2013 -sartika-
  28. 28. Questionnaire of Tourist/customer Satisfaction • Satisfaction Indicators by Parasuraman Satisfaction Indicators 1 Tangible: - Hotel facilities - ... Reliability: - Value of the product - ... Responsiveness: - ... - ... Assurance: - ... - ... Empathy: - ... - ... 12/12/2013 -sartika- 2 3 4 5
  29. 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 12/12/2013 -sartika-
  30. 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, ... 12/12/2013 -sartika-
  31. 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. 12/12/2013 -sartika-
  32. 32. Thank you
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×