文章信息

作者： kaiwu

日期：2024年11月14日

点击数：157

• Chinese
• English
• data
• bibliometrics
• R software
• python
• course: tourism research methods
• kaiwu

 

simulated dataset (tourist satisfaction)

1. questionnaire

     http://kaiwu.city/openfiles/tourist_satisfaction_questionnaire_en.docx

    http://kaiwu.city/openfiles/tourist_satisfaction_questionnaire_en.pdf 

No.	variable	variable labels
1	sid	Respondent ID
2	gender	gender
3	byear	Year of birth
4	region	region
5	income	monthly income
6	expense	daily expense
7	type3	Destination type: nature, culture and mix
8	type2	Destination type: sightseeing vs. participation
9	thotel	Types of hotel
10	sat1	satisfaction: scenery
11	sat2	satisfaction: hotel
12	sat3	satisfaction: food
13	sat4	satisfaction: transportation
14	sat5	satisfaction: travel agency
15	sat6	satisfaction: shopping
16	ri1	Importance: amount
17	ri2	Importance: publishing date
18	ri3	Importance: relevance
19	ri4	Importance: positive
20	ri5	Importance: credibility
21	rp1	Performance: amount
22	rp2	Performance: publishing date
23	rp3	Performance: relevance
24	rp4	Performance: positive
25	rp5	Performance: credibility
26	te1	thrill
27	te2	indulgence
28	te3	enjoyment
29	te4	excitement
30	te5	liberaty
31	te6	freedom
32	te7	refreshment
33	te8	revitalization
34	zh1	uncomfortable when there are many people
35	zh2	I dislike interpersonal communication
36	zh3	I’m not good at interpersonal communication
37	zh4	I try to avoid face-to-face communication
38	zh5	Going out is a tiring thing for me.
39	zh6	I try to avoid going out
40	zh7	It’s troublesome for me to go out.
41	latitude	latitude
42	longitude	longitude

 

value labels
Value labels gender
1	male
2	female.
Value labels region
1	Central China
2	East China
3	North China
4	Northeast China
5	Northwest China
6	Southwest China
7	West China.
Value labels type3
1	natural scenery
2	historical scenery
3	mixed scenery.
Value labels type2
1	sightseeing
2	participation.
Value labels thotel
1	budget hotel
2	luxury hotel
3	bed and breakfast
4	apartment hotel.

 

2. dataset

2.1 csv file

general purpose (without variable labels and value labels)

  http://kaiwu.city/openfiles/tourist.csv

    or

2.2 Excel file

    http://kaiwu.city/openfiles/data_tourist_satisfaction_en.xlsx 

    or

2.3 SPSS file

    http://kaiwu.city/openfiles/data_tourist_satisfaction_en.sav

 

 

3. python ipynb file

  http://kaiwu.city/openfiles/tourist_EN2024Nov10.ipynb 

 

 

4.results

     http://kaiwu.city/openfiles/academic_report_SPSS_EN.docx

 

---

 

3.1 frequency Analysis（discrete variables）

https://libguides.library.kent.edu/SPSS/FrequenciesCategorical

https://www.spss-tutorials.com/spss-frequencies-command/ 

https://datatab.net/tutorial/frequency-table

 

3.2 cross-table（discrete variables）

https://libguides.library.kent.edu/SPSS/Crosstabs 

 

3.3 Descriptive analysis（continuous variables）

https://libguides.library.kent.edu/SPSS/Descriptives

https://www.spss-tutorials.com/spss-descriptives-command/

 

3.4 custom table

https://www.ibm.com/docs/en/spss-statistics/saas?topic=statistics-custom-tables

http://kaiwu.city/index.php/spss-custom-table

 

 

4.relationship between two variables

types of variables (level of measurment)

https://statistics.laerd.com/statistical-guides/types-of-variable.php

https://www.thoughtco.com/independent-and-dependent-variable-examples-606828

https://datatab.net/tutorial/level-of-measurement

4.1 chi-square test

https://libguides.library.kent.edu/SPSS/ChiSquare

https://datatab.net/tutorial/chi-square-test

 

4.2 independent sample t-test

https://libguides.library.kent.edu/SPSS/IndependentTTest

https://datatab.net/tutorial/unpaired-t-test

 

4.3 ANOVA

https://libguides.library.kent.edu/SPSS/OneWayANOVA

https://datatab.net/tutorial/anova

 

4.4 correlation

https://libguides.library.kent.edu/SPSS/PearsonCorr 

https://datatab.net/tutorial/correlation

 

4.5 logistic regression

https://www.spss-tutorials.com/logistic-regression/

https://datatab.net/tutorial/logistic-regression

 

5.analysis for scales (measurement)

5.1 reliability analysis

https://www.spss-tutorials.com/cronbachs-alpha-in-spss/

https://www.spss-tutorials.com/spss-split-half-reliability/

https://datatab.net/tutorial/cronbachs-alpha

 

5.2 exploratory factor analysis (EFA) for validity analysis

https://www.spss-tutorials.com/spss-factor-analysis-tutorial/

https://www.spss-tutorials.com/spss-factor-analysis-intermediate-tutorial/

https://www.spss-tutorials.com/apa-reporting-factor-analysis/

https://datatab.net/tutorial/exploratory-factor-analysis

 

6.export result

https://www.spss-tutorials.com/spss-output/

https://www.spss-tutorials.com/spss-apa-format-descriptives-tables/

 

 

文章信息

作者： kaiwu

日期：2024年11月06日

点击数：154

• Chinese
• English
• data
• bibliometrics
• R software
• python
• course: tourism research methods
• kaiwu

 

1.introduction to python

about python

http://kaiwu.city/index.php/python

books on  python

http://kaiwu.city/index.php/python-book

 

2.install python

python 3.13 you can download from official website

https://www.python.org/downloads/ 

or

 http://kaiwu.city/openfiles/python-3.13.0-amd64.exe 

 

http://kaiwu.city/index.php/python-vscode

 

3.python and Excel

    http://kaiwu.city/openfiles/hotel50python_EN.xlsx 

  http://kaiwu.city/openfiles/python_excel50hotel_EN.ipynb 

 

import openpyxl as xl; # be sure to modify the folder where the files are stored.  # The names of the folders should not contain non-English characters and spaces. filename ="D:/tdata/hotel50/hotel50python_EN.xlsx" wb1 = xl.load_workbook(filename) ws1 = wb1.worksheets[0] mr = ws1.max_row mc = ws1.max_column # load the workbook from openpyxl import Workbook # create a new workbook wb2 = Workbook() ws2 =  wb2.active # The "i" represents the row number.  # create a workbook for the data from row 2 to row 50 respectively. for i in range (2, mr + 1):     #The name of the new worksheet: Hotel Name     ws2.title = ws1.cell(row = i, column = 3).value           # j is the column number from 1 to 8:id, hotelid,Hotel Name,Rating,City,Address,Opening Year,Number of Rooms          for j in range (1, mc):         #Copy the first row of the source data, which is the header row, and use it as the first row of the new worksheet.         ws2.cell(row = 1, column = j).value = ws1.cell(row = 1, column = j).value                  # Copy the data in the i-th row of the source data and use it as the 2nd row of the new worksheet.         ws2.cell(row = 2, column = j).value = ws1.cell(row = i, column = j).value     # Save the created Excel workbook.     wb2.save("D:/tdata/hotel50/" + ws2.title + ".xlsx")     # Output the result to indicate the work progress, specifically which worksheet has been completed already.     print('save No. %d Excel file。' % (i-1))

 

文章信息

作者： kaiwu

日期：2024年10月24日

点击数：215

• Chinese
• English
• tourism research methods
• information management in tourism and hospitality
• statistics
• Excel
• dataset

1. basic of excel

http://kaiwu.city/index.php/online-office-en

 

 tengxun document

https://docs.qq.com

2.download the  simulated dataset

2.3 data for Excel (with variable labels and value labels)

   http://kaiwu.city/openfiles/data_tourist_satisfaction_enlabels2024.xlsx

 

 

 

 

 

文章信息

作者： kaiwu

日期：2024年9月29日

点击数：281

• English
• website
• artificial intelligence

https://www.ibm.com/cn-zh/topics/generative-ai

Gen AI applications are a type of artificial intelligence that utilise Gen AI ;various processes including deep learning models, complex algorithms and neural networks, by accessing large training datasets to produce their own original content. They can be classed as machine learning technology. Students may use Gen AI applications to generate new content including text, sound, images, or video. They do this by analyzing patterns in existing data and then creating output, sometimes shaped by prompts and input from the user.

The term Gen AI refers to computational techniques that are capable of generating seemingly new, meaningful content such as text, images, or audio from training data. 

1. transform model

tranditional article of transform model

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention Is All You Need (arXiv:1706.03762). arXiv. https://doi.org/10.48550/arXiv.1706.03762

 

video for transform model

https://www.bilibili.com/video/BV1gG411a7bw/

 

2.international service

2.1 chatGPT from openAI

https://chat.openai.com/

 

2.2 gemini from google

https://gemini.google.com/

 

2.3 claude from anthropic

https://www.anthropic.com/claude

 

2.4 llama from meta

https://llama.meta.com/llama3/

 

3. Chinese service

3.1 metaso

https://metaso.cn/

3.2 kimi

https://kimi.moonshot.cn/

3.3 yiyan from baidu.com

https://yiyan.baidu.com/

3.4 tongyi from aliyun

https://tongyi.aliyun.com/ 

3.5 xunfei from iflytek

https://xinghuo.xfyun.cn/desk

3.6 hunyuan from tencent

https://hunyuan.tencent.com/

3.7 doubao from bytedance

https://www.doubao.com/chat 

 

3.8 suno (for music)

https://suno.g-mi.cn/#/

 

 

 

4.practice

4.1 search answers

prompt：authenticity in tourism experience

https://metaso.cn/

 

4.2 search codes for programming

prompt：how to import json dataset using python

https://www.doubao.com/chat 

 

4.3 summary

https://www.doubao.com/chat 

 

Wang, N. (1999). Rethinking Authenticity in Tourism Experience. Annals of Tourism Research, 26(2), 349–370. https://doi.org/10.1016/S0160-7383(98)00103-0

http://kaiwu.city/openfiles/rethinking_authenticity1999wang_ning.pdf

 

prompt：Please summarize this article in 300 words

 

 

4.4 OCR(Optical Character Recognition)

 https://www.doubao.com/chat 

prompt：please OCR this image, and organize the information in a table

 

4.5 translation

https://www.doubao.com/chat 

prompt：please translate this into Chinese

prompt：please show me 8 editions of translation

 

4.6 data analysis

https://www.doubao.com/chat

  http://kaiwu.city/openfiles/tourist.csv

prompt：please summarize this dataset

prompt：how to use python to conduct frequency analysis for gender variable of this dataset

 

4.7 TTS (text to speech)

https://elevenlabs.io/ 

 ----

https://en.wikipedia.org/wiki/Generative_artificial_intelligence 

Generative artificial intelligence (generative AI, GenAI,^[1] or GAI) is artificial intelligence capable of generating text, images, videos, or other data using generative models,^[2] often in response to prompts.^[3][4] Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics.^[5][6]