FACILITATING STATISTICAL ANALYSIS OF TEXTUAL...

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Purdue Tourism & Hospitality Research Center FACILITATING STATISTICAL ANALYSIS OF TEXTUAL DATA: A TWO-STEP APPROACH Svetlana Stepchenkova Andrei P. Kirilenko Alastair M. Morrison

Transcript of FACILITATING STATISTICAL ANALYSIS OF TEXTUAL...

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FACILITATING STATISTICAL ANALYSIS OF TEXTUAL DATA:

A TWO-STEP APPROACH

Svetlana StepchenkovaAndrei P. Kirilenko

Alastair M. Morrison

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Presentation Outline

• Introduction• Proposed methodology• Example 1: Russia’s affective images• Example 2: Organic images of China &

Russia• Discussion

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Introduction• Analysis of destination images provide insights into

consumer travel behavior (Ahmed, 1991)• Strong preference for structured image measurement

(Gallarza et al, 2002; Pike, 2002)• Comparative advantages of structured and

unstructured methodologies• Holistic images: theoretically researched but not

adequately tested• Large amounts of available data

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Introduction (cont.)

• Computer-assisted procedure• Aid in identification of image variables• Efficiently deal with a large number of

text blocks• Assist in “cleaning up” the data

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Two-Step Approach

Analysis of textual data with specialized software

Statistical analysis

1. Identification of destination image variables in textual files using CATPAC (Woelfel, 1998)

2. Counting the occurrences of identified variables in every textual file with WORDER (Kirilenko, 2004): 1000 files, 1000 variables

Statistical analysis of frequencies matrix with general-purpose statistical software (SPSS, SAS)

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Worder The problems of content analysis, such as different spellings, multi-word concepts, synonyms, singular/plural, negatives, are addressed and solved by WORDER. depressed

depression

despair

hopeless

dreary

drab

dark

darker

gloomy

glum

bleak

gray

grey

depressing

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Application Example 1

• Russia’s Destination Image online survey (Stepchenkova, 2005)

• Followed conceptual framework by Echtner & Ritchie (1993)

• “What images or characteristics come to mind when you think of Russia as a travel destination?” (psychological holistic, or affective, images)

• 337 survey responses, 317 textual responses

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Step 1: CATPAC

• Found all evaluative descriptors (around 240) in textual responses

• Combined them into 42 synonymic groups. One word for each group was selected as affective image variable

Example of a synonymic group:Austere, stark, Spartan, primitive, stoic, lack, minimal

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Step 2: WORDER• In the textual data words belonging to the same synonymic

group were replaced by the representative image variable

• Frequencies of all 42 affective image variables were counted by WORDER and entered into SPSS

WORDER Input Table (.csv format)

alcoholism alcohol vodka drugs drink too muchaustere stark spartan primitive stoic lack

minimalawesome awe beautiful great glorious grandeur

incredible wonderful wondrous

boring dull bland indifferencelack of enthusiasm

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Affective Image VariablesVariable Freq Variable Freq Variable Freqfriendly 85 free 11 alcoholism 6somber 47 open 11 hardworking 6depressing 45 interesting 11 festive 5unfriendly 28 austere 11 contrasts 5cold 18 hostility 10 happy 5poor 18 unhappy 10 uncomfortable 5reserved 17 pleasant 10 serene 4exciting 15 difficult 9 safe 4tense 15 sad 8 hopeful 4unsafe 15 cosmopolitan 8 ruthless 4good 15 cordial 8 seedy 4upbeat 14 cautious 7 historical 4awesome 14 boring 7 unpleasant 3undeveloped 13 fascinating 7 relaxing 2

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Favorability: OperationalizationAffective Variable Favorability Scores: Averaged evaluations

of 35 experts. Scale: -2, -1, 0, +1, +2 Variable Score Variable Score Variable Score fascinating 1.97 serene 1.53 tense -1.11 friendly 1.92 relaxing 1.47 boring -1.19 happy 1.83 cosmopolitan 1.44 difficult -1.19 exciting 1.81 upbeat 1.43 seedy -1.28 festive 1.78 free 1.36 uncomfortable -1.36 good 1.72 open 1.36 sad -1.42 awesome 1.72 contrasts 1.06 hostility -1.44 hardworking 1.69 reserved 0.08 ruthless -1.53 historical 1.67 cold -0.31 unhappy -1.56 safe 1.64 cautious -0.33 unfriendly -1.64 interesting 1.61 somber -0.39 depressing -1.67 pleasant 1.58 austere -0.41 unpleasant -1.68 cordial 1.56 undeveloped -0.58 alcoholism -1.75 hopeful 1.53 poor -1.00 unsafe -1.78

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Favorability Variable OperationalizedResponse example:“Fascinating country. Overall, people are caring but emotionally controlled. Boring nightlife, dull food, though”:“Fascinating country. Overall, people are friendly but reserved. Boring nightlife, boring food, though”:

Fascinating = 1.97; Friendly = 1.92; Reserved = 0.08; Boring = -1.19

1.97+1.92+0.08-1.19-1.19 = 1.59

Descriptive Statistics

337 -6.0832 8.7222 .326728 2.2402324337

FavorabilityValid N (listwise)

N Minimum Maximum Mean Std. Deviation

WORDER

EXPERTS

Response overall favorability value

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Hypothesis testing: ResultsHypothesis: People who have visited Russia previously have more favorable images of Russia than those who have not.

Levene's Test for Equality of

Variances t-test for Equality of

Means Visitation N Mean F Sig. t df p-value

visitors 54 0.808 3.572 0.060 1.726 335 0.085 non-visitors 283 0.235

n1=54: Normal distribution confirmed

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Application Example 2• Comparison of organic images of China and Russia in the

U.S. media (Stepchenkova, Chen, & Morrison, 2005)• 2002-2004 general news articles from U.S. regional

sources: Midwest, Northeast, Southeast, and Western.• LexisNexis database: Words “China” or “Russia” in

headlines.• Systematic random sampling: 540 + 540, 15 per month.

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Two-Step Approach

• Step 1. CATPAC: Organic image variables, frequencies.• Step 2. WORDER: Counted the identified image variables

in every file of “Chinese” and “Russian” samples.

In SPSS: Factor Analysis to identify organic image themesQualitative part:Assignment of a favorability rating to organic image themesFavorability comparisons of organic images

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Organic Image Variables

companies 658 city 316 industry 216 steel 190government 586 Taiwan 302 u.s. 213 growth 190military 499 foreign 275 power 212 leaders 184country 474 state 268 Bush 209 job 179market 462 president 265 technology 207 Shanghai 178official 433 economic 246 economy 201 American 176trade 382 price 238 high 198 Asia 174officials 355 Hong Kong 228 school 196 cost 173

Moscow 729 people 428 Iraq 312 weapons 274Putin 657 government 421 international 310 US 269year 530 states 418 country 308 former 263united 472 time 409 against 302 security 258world 461 nuclear 389 percent 296 military 258president 459 war 366 company 291 foreign 254Soviet 454 American 324 officials 286 Bush 252oil 448 state 317 million 278 Kremlin 246

China

Russia

(first 32)

(first 32)

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Factor Analysis: China and Russia• Number of subjects: 540 and 540• Number of variables: 83 and 70

• KMO statistic of sampling adequacy: 0.766 and 0.752• Bartlett’s test: p < 0.0001• Principal Components Analysis• Direct Oblimin Rotation, allows factors to co-vary (Kline,

1994)• Number of factors specified: 15• Variance explained: 57.2% and 58.1%

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China: Main Organic Image Themes

0.7792.152students, teamEducational Exchange15

0.6632.484security, council, againstSecurity Concerns14

0.4232.909power, nuclear, American, KoreaAsian Politics13

0.5832.940Wang, local, communist party, Communist China12

0.9403.121human rights, world, yearHuman Rights11

0.6993.140health, disease, SARS, news, province, city, Hong KongSARS10

0.6363.161public, school, Shanghai, U.S., old (year-old), helpCultural Communication9

0.6723.176job, high, work, construction, centerLabor Market8

0.7843.537Jiang, Hu, Wen, leaders, presidentGovernment7

0.7453.943administration, technology, Bush, sales, export, officials, LiTechnology Transfer6

0.7263.967military, official, government, Taiwan, Asia, WashingtonTaiwan5

0.6234.048state, major, global, market, university, ChineseGlobal Market4

0.7124.464WTO, trade, foreign, import, countryWorld Trade Organization (WTO)3

0.7264.717demand, price, cost, steel, industry, company, workersIndustry2

0.5875.911economy, bank, money, growth, central, investment, economic, ChinaEconomic Growth1

Cronbach’s Alpha

Varianceexplained

ItemsFactorNo.

0.7792.152students, teamEducational Exchange15

0.6632.484security, council, againstSecurity Concerns14

0.4232.909power, nuclear, American, KoreaAsian Politics13

0.5832.940Wang, local, communist party, Communist China12

0.9403.121human rights, world, yearHuman Rights11

0.6993.140health, disease, SARS, news, province, city, Hong KongSARS10

0.6363.161public, school, Shanghai, U.S., old (year-old), helpCultural Communication9

0.6723.176job, high, work, construction, centerLabor Market8

0.7843.537Jiang, Hu, Wen, leaders, presidentGovernment7

0.7453.943administration, technology, Bush, sales, export, officials, LiTechnology Transfer6

0.7263.967military, official, government, Taiwan, Asia, WashingtonTaiwan5

0.6234.048state, major, global, market, university, ChineseGlobal Market4

0.7124.464WTO, trade, foreign, import, countryWorld Trade Organization (WTO)3

0.7264.717demand, price, cost, steel, industry, company, workersIndustry2

0.5875.911economy, bank, money, growth, central, investment, economic, ChinaEconomic Growth1

Cronbach’s Alpha

Varianceexplained

ItemsFactorNo.

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Russia: Main Organic Image Themes

0.6781.871Beslan, schoolTerrorism15

0.7252.318space, station, programU.S.-Russia Space Cooperation14

0.4892.444team, national, world, timeSports13

0.6352.778power, place (take place), investmentPower Sector Reform 12

0.5932.823Chinese, China, RussiansRussia-China Relations11

0.5122.841NATO, defense, Bush, countriesNATO10

0.6733.198family, home, children, year-old, wantRussian Children in the U.S.9

0.7303.291nuclear, weapons, Iran, securityIran8

0.7203.519Soviet, Union, former, yearsSoviet Past7

0.6673.554Chechen, Chechnya, people, war, MoscowChechnya6

0.6903.691oil, percent, gas, Russia’s Russia, million, governmentNatural Monopolies5

0.5953.849law, foreign, Russian, international, against, countryLaw4

0.7474.110election, Vladimir, Putin, political, party, Kremlin, presidentPresidential Elections3

0.7764.732United, States, Iraq, nations, AmericanIraq2

0.8104.936Yukos, company, Khodorkovsky, state, billion, business, companiesYukos1

Cronbach’s Alpha

Varianceexplained

ItemsFactorNo.

0.6781.871Beslan, schoolTerrorism15

0.7252.318space, station, programU.S.-Russia Space Cooperation14

0.4892.444team, national, world, timeSports13

0.6352.778power, place (take place), investmentPower Sector Reform 12

0.5932.823Chinese, China, RussiansRussia-China Relations11

0.5122.841NATO, defense, Bush, countriesNATO10

0.6733.198family, home, children, year-old, wantRussian Children in the U.S.9

0.7303.291nuclear, weapons, Iran, securityIran8

0.7203.519Soviet, Union, former, yearsSoviet Past7

0.6673.554Chechen, Chechnya, people, war, MoscowChechnya6

0.6903.691oil, percent, gas, Russia’s Russia, million, governmentNatural Monopolies5

0.5953.849law, foreign, Russian, international, against, countryLaw4

0.7474.110election, Vladimir, Putin, political, party, Kremlin, presidentPresidential Elections3

0.7764.732United, States, Iraq, nations, AmericanIraq2

0.8104.936Yukos, company, Khodorkovsky, state, billion, business, companiesYukos1

Cronbach’s Alpha

Varianceexplained

ItemsFactorNo.

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Favorability Rating: China• Favorable (+1):

– Economic Growth– Industry– WTO– Global Market– Technology Transfer– Cultural

Communications– Educational Exchange

• Unfavorable (-1):– Taiwan– SARS– Human Rights– Communist China

• Neutral (0):– China’s Government– Labor Market– Asian Politics– Security Concerns

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Favorability Rating: Russia• Favorable (+1):

– Russian Children in the U.S.– Power Sector Reform– Sports– U.S.-Russia Space

Cooperation

• Unfavorable (-1):– Yukos– Iraq– Chechnya– Iran– Law– Terrorism• Neutral (0):

- Presidential Elections - Natural Monopolies - Russia-China Relationships- Soviet Past- NATO

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Comparison of Organic Images

-6

-4

-2

0

2

4

6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Factors

Vari

an

ce e

xp

lain

ed

RussiaChina

Economic Growth

Sports

Yukos

SARS

Terrorism

Technology Transfer Favorable

Unfavorable

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Aggregated Organic Image Themes

Chechnya; TerrorismSARSSafety

Presidential ElectionsGovernment; TaiwanInternal affairs

Iraq; Iran; NATO; Russia-China Relations; Soviet Past

Asian Politics; Security ConcernsForeign policy

Russian Children in the U.S.; Sports; U.S.-Russia Space Cooperation

Global Market; Cultural Communications; Educational Exchange

Exchange(education, technology, sports, human relations)

Yukos; Natural Monopolies; Power Sector Reform

Economic Growth; Industry; WTO; Global Market; Technology Transfer; Labor Market

Economy

Yukos; Law; ChechnyaHuman Rights; Communist ChinaHuman rightsRussiaChinaCategory

Chechnya; TerrorismSARSSafety

Presidential ElectionsGovernment; TaiwanInternal affairs

Iraq; Iran; NATO; Russia-China Relations; Soviet Past

Asian Politics; Security ConcernsForeign policy

Russian Children in the U.S.; Sports; U.S.-Russia Space Cooperation

Global Market; Cultural Communications; Educational Exchange

Exchange(education, technology, sports, human relations)

Yukos; Natural Monopolies; Power Sector Reform

Economic Growth; Industry; WTO; Global Market; Technology Transfer; Labor Market

Economy

Yukos; Law; ChechnyaHuman Rights; Communist ChinaHuman rightsRussiaChinaCategory

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Comparison of Organic Images

0123456

Human rights

Economy

Exchange

Foreign policy

Internal affairs

Safety

RussiaChina

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Discussion• Large number of similar textual files (survey

responses, newspaper articles, etc.)• Different types of textual data• Enhancement of numerical data matrices by contextual

variables – data source, type, length, time, etc. (visitors/non-visitors in Ex. 1)

• “Embellishment” by secondary variables, e.g., “favorable-unfavorable”

• Flexibility of the two-step approach: can be combined with quantitative (Ex. 1) and qualitative (Ex. 2) methods

• “Black box” issue

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Discussion (cont.)

• Interpretational dimension (latent vs. manifest variables)

• Structural dimension: thematic, semantic, or neural (Roberts, 2000)

• Generalization issue• Appropriateness of the approach

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Selected References• Alexa, M. and Zuell, C. (2000). Text analysis software: Commonalities, differences and limitations: The

results of a review. Quality and Quantity, 34, 299-321.• Echtner, C. M. and Ritchie, J.R. B. (1993). The measurement of destination image: An empirical assessment.

Journal of Travel Research, 31(4), 3-13. • Gray, J. H. and Densten, I. L. (1998). Integrating quantitative and qualitative analysis using latent and

manifest variables. Quality and Quantity, 32, 419-431.• Jenkins, O. H. (1999). Understanding and measuring tourist destination images. The International Journal of

Tourism Research, 1(1), 1-15. • Insch, G. S. and Moore, J. E., (1997). Content analysis in leadership research: Examples, procedures, and

suggestions for future use. Leadership Quarterly, 8(1), 1-25.• Kirilenko, A. P. (2004). WORDER (Version 2.0) [Computer software]. Http://kirilenko.org/worder.

[Accessed the 18th of November 2005, 10:20]. • Krippendorf, K. (1980). Content analysis: An introduction to its methodology. Newbury Park, CA: Sage.• Pike, S. (2002). Destination image analysis – a review of 142 papers from 1973 to 2000. Tourism

Management, 23, 541-549.• Roberts, C. W. (2000). A conceptual framework for quantitative text analysis. Quality and Quantity, 34, 259-

274.• Stepchenkova, S. (2005). Russia’s destination image among American pleasure travelers. Master’s thesis.

Purdue University, West Lafayette, IN.• Stepchenkova, S., Chen, Y. and Morrison, A. M. (2005). China and Russia: A comparative analysis of

organic destination images. The 11th APTA Conference Proceedings, Vol. 1. New Tourism for Asia-Pacific (pp. 273-283).

• Weber, R. P. (1985). Basic content analysis. Beverly Hills, CA: Sage.• Woelfel, J. (1998). CATPAC: Users guide. New York, NY.: RAH Press: The Galileo Company.

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Thank you!

Questions?