ANALISIS DATA SARTIKA SARI

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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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Simplifying process easily be

understood

DATA ANALYSIS

QUALITATIVE MIXED QUANTITATIVE

BASIC CONCEPT

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CHOOSE BASED ON CHARACTERISTICS OF

THE DATA

QUALITATIVE

QUANTITATIVE

EXAMPLES

- Quality of life of the local

community in Ubud

- Local perception of tourism as

an indicator of destination

decline

- Comparative analysis of

students’ achievement

between girls and boys in

tourism institute

- The effect of increase fuel

price towards local tourist

arrival

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QUALITATIVE

DESCRIBING

CLASSIFYING CONNECTING

Dey, (1993: 32)

Circular process of qualitative analysis

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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 reduction Drawing /

verifying

conclusion

Data display

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• 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

A process of ...

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QUALITATIVE

Pengamatan deskriptif

Pengamatan terfokus

Pengamatan terpilih

Component analysis

Taxonomy analysis

Domain analysis

Beginning of the research End of the research

Model by James P. Spradley

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• finding out the description as a whole about the problem being analyzed

• description the universal semantic relationship (9 types)

• Example:

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DOMAIN taxonomy component ...

No Semantic Relationship Sample Forms

1 Form/Jenis 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 Characteristic/ karakteristik

X adalah karakteristik dari Y

• Mahasiswa asing (pertukaran mhs Indonesia - Belanda)

• Domain yg berkaitan dg jenis: (studi yang diambil, kegiatan sehari-hari, 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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Listing domain based on the fact

formulate question for each domain

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• 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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domain TAXONOMY component ...

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• 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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domain TAXONOMY component ...

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Guiding licence function

Individual identity

lifelihood Legal

profession

Association identity

Members community

Working channel

Working access

Enter all destinations

Guiding in all places

Credibility

Confidence in guiding

Giving trust to tourist

Giving safety and

security

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domain TAXONOMY component ...

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domain taxonomy COMPONENT ...

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• 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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domain taxonomy component THEMES

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• 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

• 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

Qualitative data analysis,

in short ...

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• 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.

Additional info for qualitative

data analysis

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KUANTITATIF

STATISTIK DESKRIPTIF STATISTIK INFERENSIAL

STATISTIK NON-PARAMETRIK

STATISTIK PARAMETRIK

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Data distribution form

– Mean

– Median

– Modus

– Standar deviasi, range, koefisien variasi

Data display

– Tabel

– Gambar/grafik

DESCRIPTIVE STATISTICS

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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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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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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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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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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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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

Non-Parametric Analysis

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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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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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Questionnaire of

Tourist/customer Satisfaction • Satisfaction Indicators by Parasuraman

Satisfaction Indicators 1 2 3 4 5

Tangible: - Hotel facilities - ...

Reliability: - Value of the product - ...

Responsiveness: - ... - ...

Assurance: - ... - ...

Empathy: - ... - ...

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“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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Based on the data above, 31% indicates the guests are not at all satisfied, 25% the guests are slightly satisfied, ...

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

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• (Class Interval )

Ci = range

K

Ci = (5-1) = 0.8

5

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.

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

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Thank you