Rotated factor matrix a
Factor | ||||||
1 | 2 | 3 | 4 | 5 | 6 | |
DL6 | .862 | |||||
DL7 | .790 | |||||
DL5 | .682 | |||||
DL4 | .632 | |||||
DL8 | .557 | |||||
DL9 | .521 | |||||
DL10 | .479 | |||||
DL2 | .400 | |||||
TD7 | .720 | |||||
TD8 | .698 | |||||
TD2 | .696 | |||||
TD3 | .642 | |||||
TD6 | .618 | |||||
TD1 | .585 | |||||
TD4 | .569 | |||||
TD5 | .527 | |||||
CQ3 | .872 | |||||
CQ2 | .845 | |||||
CQ1 | .761 | |||||
CQ4 | .669 | |||||
DL11 | .701 | |||||
DL1 | .649 | |||||
DL3 | .562 | |||||
KN2 | .783 | |||||
KN1 | .744 | |||||
KN3 | .727 | |||||
VC2 | .831 | |||||
VC3 | .793 | |||||
VC1 | .787 | |||||
Maybe you are interested!
-
Research on factors affecting domestic tourists' satisfaction with ecotourism in Ben Tre province - 21 -
Research on factors affecting domestic tourists' satisfaction with ecotourism in Ben Tre province - 20 -
Identify Rating Levels and Rating Scales
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of the islanders. Therefore, this indicator will be divided into two sub-indicators:
a1. Natural tourism attractiveness a2. Cultural tourism attractiveness
b. Tourist capacity
The two island communes in Quan Lan have different capacities to receive tourists. Minh Chau Commune is home to many standard hotels and resorts, attracting high-income domestic and international tourists. Meanwhile, Quan Lan Commune has many motels mainly built and operated by local people, so the scale and quality are not high, and will be suitable for ordinary tourists such as students.
c. Time of exploitation of Quan Lan Island Commune:
Quan Lan tourism is seasonal due to weather and climate conditions and festivals only take place on certain days of the year, specifically in spring. In Quan Lan commune, the period from April to June and from September to November is considered the best time to visit Quan Lan because the cultural tourism activities are mainly associated with festivals taking place during this time.
Minh Chau island commune:
Tourism exploitation time is all year round, because this is a place with a number of tourist attractions with diverse ecosystems such as Bai Tu Long National Park Research Center, Tram forest, Turtle Laying Beach, so besides coming to the beach for tourism and vacation in the summer, Minh Chau will attract research groups to come for tourism combined with research at other times of the year.
d. Sustainability
The sustainability of ecotourism sites in Quan Lan and Minh Chau communes depends on the sensitivity of the ecosystems to climate changes.
landscape. In general, these tourist destinations have a fairly high level of sustainability, because they are natural ecosystems, planned and protected. However, if a large number of tourists gather at certain times, it can exceed the carrying capacity and affect the sustainability of the environment (polluted beaches, damaged trees, animals moving away from their habitats, etc.), then the sustainability of the above ecosystems (natural ecosystems, human ecosystems) will also be affected and become less sustainable.
e. Location and accessibility
Both island communes have ports to take tourists to visit from Van Don wharf:
- Quan Lan – Van Don traffic route:
Phuc Thinh – Viet Anh high-speed boat and Quang Minh high-speed boat, depart at 8am and 2pm from Van Don to Quan Lan, and at 7am and 1pm from Quan Lan to Van Don. There are also wooden boats departing at 7am and 1pm.
- Van Don - Minh Chau traffic route:
Chung Huong high-speed train, Minh Chau train, morning 7:30 and afternoon 13:30 from Van Don to Minh Chau, morning 6:30 and afternoon 13:00 from Minh Chau to Van Don.
f. Infrastructure
Despite receiving investment attention, the issue of infrastructure and technical facilities for tourism on Quan Lan Island is still an issue that needs to be resolved because it has a direct impact on the implementation of ecotourism activities. The minimum conditions for serving tourists such as accommodation, electricity, water, communication, especially medical services, and security work need to be given top priority. Ecotourism spots in Minh Chau commune are assessed to have better infrastructure and technical facilities for tourism because there are quite complete and synchronous conditions for serving tourists, meeting many needs of domestic and foreign tourists.
3.2.1.4. Determine assessment levels and assessment scales
Corresponding to the levels of each criterion, the index is the score of those levels in the order of 4, 3, 2, 1 decreasing according to the standard of each level: very attractive (4), attractive (3), average (2), less attractive (1).
3.2.1.5. Determining the coefficients of the criteria
For the assessment of DLST in the two communes of Quan Lan and Minh Chau islands, the students added evaluation coefficients to show the importance of the criteria and indicators as follows:
Coefficient 3 with criteria: Attractiveness, Exploitation time. These are the 2 most important criteria for attracting tourists to tourism in general and eco-tourism in particular, so they have the highest coefficient.
Coefficient 2 with criteria: Capacity, Infrastructure, Location and accessibility . Because the assessment area is an island commune of Van Don district, the above criteria are selected by the author with appropriate coefficients at the average level.
Coefficient 1 with criteria: Sustainability. Quan Lan has natural and human-made ecotourism sites, with high biodiversity and little impact from local human factors. Most of the ecotourism sites are still wild, so they are highly sustainable.
3.2.1.6. Results of DLST assessment on Quan Lan island
a. Assessment of the potential for natural tourism development
For Minh Chau commune:
+ Natural tourism attractiveness is determined to be very attractive (4 points) and the most important coefficient (coefficient 3), so the score of the Attractiveness criterion is 4 x 3 = 12.
+ Capacity is determined as average (2 points) and the coefficient is quite important (coefficient 2), then the score of Capacity criterion is 2 x 2 = 4.
+ Exploitation time is long (4 points), the most important coefficient (coefficient 3) so the score of the Exploitation time criterion is 4 x 3 = 12.
+ Sustainability is determined as sustainable (4 points), the important coefficient is the average coefficient (coefficient 1), so the score of the Sustainability criterion is 4 x 1 = 4 points
+ Location and accessibility are determined to be quite favorable (2 points), the coefficient is quite important (coefficient 2), the criterion score is 2 x 2 = 4 points.
+ Infrastructure is assessed as good (3 points), the coefficient is quite important (coefficient 2), then the score of the Infrastructure criterion is 3 x 2 = 6 points.
The total score for evaluating DLST in Minh Chau commune according to 6 evaluation criteria is determined as: 12 + 4 + 12 + 4 + 4 + 6 = 42 points
Similar assessment for Quan Lan commune, we have the following table:
Table 3.3: Assessment of the potential for natural ecotourism development in Quan Lan and Minh Chau communes
Attractiveness of self-tourismof course
Capacity
Mining time
Sustainability
Location and accessibility
Infrastructure
Result
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
CommuneMinh Chau
12
12
4
8
12
12
4
4
4
8
6
8
42/52
Quan CommuneLan
6
12
6
8
9
12
4
4
4
8
4
8
33/52
b. Assessment of the potential for humanistic tourism development
For Quan Lan commune:
+ The attractiveness of human tourism is determined to be very attractive (4 points) and the most important coefficient (coefficient 3), so the score of the Attractiveness criterion is 4 x 3 = 12.
+ Capacity is determined to be large (3 points) and the coefficient is quite important (coefficient 2), then the score of the Capacity criterion is 3 x 2 = 6.
+ Mining time is average (3 points), the most important coefficient (coefficient 3) so the score of the Mining time criterion is 3 x 3 = 9.
+ Sustainability is determined as sustainable (4 points), the important coefficient is the average coefficient (coefficient 1), so the score of the Sustainability criterion is 4 x 1 = 4 points.
+ Location and accessibility are determined to be quite favorable (2 points), the coefficient is quite important (coefficient 2), the criterion score is 2 x 2 = 4 points.
+ Infrastructure is rated as average (2 points), the coefficient is quite important (coefficient 2), then the score of the Infrastructure criterion is 2 x 2 = 4 points.
The total score for evaluating DLST in Quan Lan commune according to 6 evaluation criteria is determined as: 12 + 6 + 6 + 4 + 4 + 4 = 36 points.
Similar assessment with Minh Chau commune we have the following table:
Table 3.4: Assessment of the potential for developing humanistic eco-tourism in Quan Lan and Minh Chau communes
Attractiveness of human tourismliterature
Capacity
Mining time
Sustainability
Location and accessibility
Infrastructure
Result
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Point
DarkMulti
Quan CommuneLan
12
12
6
8
9
12
4
4
4
8
4
8
39/52
Minh CommuneChau
6
12
4
8
12
12
4
4
4
8
6
8
36/52
Basically, both Minh Chau and Quan Lan localities have quite favorable conditions for developing ecotourism. However, Quan Lan commune has more advantages to develop ecotourism in a humanistic direction, because this is an area with many famous historical relics such as Quan Lan Communal House, Quan Lan Pagoda, Temple worshiping the hero Tran Khanh Du, ... along with local festivals held annually such as the wind praying ceremony (March 15), Quan Lan festival (June 10-19); due to its location near the port and long exploitation time, the beaches in Quan Lan commune (especially Quan Lan beach) are no longer hygienic and clean to ensure the needs of tourists coming to relax and swim; this is also an area with many beautiful landscapes such as Got Beo wind pass, Ong Phong head, Voi Voi cave, but the ability to access these places is still very limited (dirt hill road, lots of gravel and rocks), especially during rainy and windy times; In addition, other natural resources such as mangrove forests and sea worms have not been really exploited for tourism purposes and ecotourism development. On the contrary, Minh Chau commune has more advantages in developing ecotourism in the direction of natural tourism, this is an area with diverse ecosystems such as at Rua De Beach, Bai Tu Long National Park Conservation Center...; Minh Chau beach is highly appreciated for its natural beauty and cleanliness, ranked in the top ten most beautiful beaches in Vietnam; Minh Chau commune is also home to Tram forest with a large area and a purity of up to 90%, suitable for building bridges through the forest (a very effective type of natural ecotourism currently applied by many countries) for tourists to sightsee, as well as for the purpose of studying and researching.
Figure 3.1: Thenmala Forest Bridge (India) Source: https://www.thenmalaecotourism.com/(August 21, 2019)
3.2.2. Using SWOT matrix to evaluate Quan Lan island tourism
General assessment of current tourism activities of Quan Lan island is shown through the following SWOT matrix:
Table 3.5: SWOT matrix evaluating tourism activities on Quan Lan island
Internal agent
Strengths- There is a lot of potential for tourism development, especially natural ecotourism and humanistic ecotourism.- The unskilled labor force is relatively abundant.- resource environmentunpolluted, still
Weaknesses- Poorly developed infrastructure, especially traffic routes to tourist destinations on the island.- The team of professional staff is still weak.- Tourism products in general
quite wild, originalintact
general and DLST in particularalone is monotonous.
External agents
Opportunity- Tourism is a key industry in the socio-economic development strategy of the province and Van Don economic zone.- Quan Lan was selected as a pilot area for eco-tourism development within the framework of the green growth project between Quang Ninh province and the Japanese organization JICA.- The flow of tourists and especially ecotourism in the world tends toincreasing
Challenge- Weather and climate change abnormally.- Competition in tourism products is increasingly fierce, especially with other localities in the province such as Ha Long, Mong Cai...- Awareness of tourists, especially domestic tourists, about ecotourism and nature conservation is not high.
Through summary analysis using SWOT matrix we see that:
To exploit strengths and take advantage of opportunities, it is necessary to:
- Diversify products and service types (build more tourism routes aimed at specific needs of tourists: experiential tourism immersed in nature, spiritual cultural tourism...)
- Effective exploitation of resources and differentiated products (natural resources and human resources)
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Research on factors affecting tourists' purchasing decisions for the 3N2D Da Nang - Hoi An tour of Dai Bang Advertising Media and Tourism Services Joint Stock Company - 11 -
Research on factors affecting the satisfaction of domestic MICE tourists in Vietnam - Taking a typical example in Hai Phong city - 28
Extraction method: Principal component analysis PCA. Rotation method: Varimax.
a. The rotation converges in 15 iterations.
Appendix 7B: Results of the second exploratory factor analysis EFA
KMO and Bartlett test
The Kaiser-Meyer-Olkin coefficient measures the goodness of fit of the sample. | .866 | |
Bartlett test | Chi-square value | 5216.210 |
df | 378 | |
Sig. | .000 | |
Total variance explained
Ingredient
Initial Eigenvalues | Sum of squares of load factors after extraction | Sum of squares of load factors after rotation | |||||||
Total | % variance | Cumulative % | Total | % variance | Cumulative % | Total | % variance | Cumulative % | |
1 | 9,274 | 33,120 | 33,120 | 9,274 | 33,120 | 33,120 | 4,962 | 17,720 | 17,720 |
2 | 2,512 | 8,971 | 42,091 | 2,512 | 8,971 | 42,091 | 3,634 | 12,980 | 30,700 |
3 | 2,244 | 8,015 | 50,106 | 2,244 | 8,015 | 50,106 | 3.323 | 11,867 | 42,567 |
4 | 1.666 | 5,948 | 56,055 | 1.666 | 5,948 | 56,055 | 2,532 | 9,045 | 51,612 |
5 | 1,391 | 4,968 | 61,022 | 1,391 | 4,968 | 61,022 | 2,172 | 7,758 | 59,369 |
6 | 1,221 | 4,362 | 65,384 | 1,221 | 4,362 | 65,384 | 1,684 | 6.015 | 65,384 |
7 | .951 | 3,395 | 68,779 | ||||||
8 | .846 | 3.020 | 71,799 | ||||||
9 | .766 | 2,734 | 74,533 | ||||||
10 | .734 | 2,622 | 77,155 | ||||||
11 | .648 | 2,315 | 79,470 | ||||||
12 | .619 | 2.211 | 81,682 | ||||||
13 | .538 | 1,923 | 83,604 | ||||||
14 | .515 | 1,838 | 85,442 | ||||||
15 | .505 | 1.805 | 87,247 | ||||||
16 | .448 | 1,600 | 88,847 | ||||||
17 | .421 | 1,504 | 90,351 | ||||||
18 | .352 | 1,257 | 91,608 | ||||||
19 | .345 | 1,231 | 92,839 | ||||||
20 | .304 | 1,086 | 93,925 | ||||||
21 | .283 | 1,012 | 94,937 | ||||||
22 | .275 | .983 | 95,920 | ||||||
23 | .228 | .816 | 96,736 | ||||||
24 | .214 | .763 | 97,499 | ||||||
25 | .200 | .715 | 98,214 | ||||||
26 | .179 | .639 | 98,853 | ||||||
27 | .172 | .614 | 99,467 | ||||||
28 | .149 | .533 | 100,000 | ||||||
Extraction method: Principal component analysis PCA
Rotated factor matrix a
Factor | ||||||
1 | 2 | 3 | 4 | 5 | 6 | |
DL6 | .847 | |||||
DL5 | .739 | |||||
DL7 | .726 | |||||
DL8 | .701 | |||||
DL4 | .681 | |||||
DL3 | .590 | |||||
DL9 | .587 | |||||
DL10 | .585 | |||||
DL1 | .513 | |||||
TD2 | .741 | |||||
TD3 | .714 | |||||
TD1 | .636 | |||||
TD5 | .546 | |||||
TD8 | .532 | |||||
TD4 | .512 | |||||
DL11 | .496 | |||||
CQ3 | .871 | |||||
CQ2 | .841 | |||||
CQ1 | .758 | |||||
CQ4 | .671 | |||||
KN2 | .794 | |||||
KN1 | .761 | |||||
KN3 | .726 | |||||
VC2 | .835 | |||||
VC1 | .792 | |||||
VC3 | .790 | |||||
TD7 | .635 | |||||
TD6 | .615 | |||||
Extraction method: Principal component analysis PCA. Rotation method: Varimax.
a. The rotation converges in 9 iterations.
Appendix 7C: Results of the third exploratory factor analysis EFA
KMO and Bartlett test
The Kaiser-Meyer-Olkin coefficient measures the goodness of fit of the sample. | .864 | |
Bartlett test | Chi-square value | 4948.771 |
df | 351 | |
Sig. | .000 | |
Total variance explained
Ingredient
Initial Eigenvalues | Sum of squares of load factors after extraction | Sum of squares of load factors after rotation | |||||||
Total | % variance | Cumulative % | Total | % variance | Cumulative % | Total | % variance | Cumulative % | |
1 | 8,894 | 32,942 | 32,942 | 8,894 | 32,942 | 32,942 | 4,744 | 17,569 | 17,569 |
2 | 2,511 | 9,300 | 42,242 | 2,511 | 9,300 | 42,242 | 3,295 | 12,205 | 29,774 |
3 | 2,244 | 8,312 | 50,554 | 2,244 | 8,312 | 50,554 | 2,974 | 11,016 | 40,790 |
4 | 1,656 | 6.134 | 56,688 | 1,656 | 6.134 | 56,688 | 2,451 | 9,079 | 49,868 |
5 | 1,388 | 5,140 | 61,828 | 1,388 | 5,140 | 61,828 | 2,170 | 8,038 | 57,906 |
6 | 1,085 | 4.018 | 65,846 | 1,085 | 4.018 | 65,846 | 2,144 | 7,939 | 65,846 |
7 | .940 | 3,481 | 69,327 | ||||||
8 | .844 | 3.127 | 72,453 | ||||||
9 | .764 | 2,829 | 75,282 | ||||||
10 | .697 | 2,581 | 77,863 | ||||||
11 | .648 | 2,399 | 80,262 | ||||||
12 | .570 | 2,110 | 82,371 | ||||||
13 | .538 | 1,991 | 84,363 | ||||||
14 | .512 | 1,897 | 86,259 | ||||||
15 | .466 | 1,724 | 87,984 | ||||||
16 | .433 | 1,605 | 89,589 | ||||||
17 | .366 | 1,355 | 90,944 | ||||||
18 | .348 | 1,290 | 92,234 | ||||||
19 | .342 | 1,266 | 93,500 | ||||||
20 | .283 | 1,049 | 94,549 | ||||||
21 | .275 | 1,020 | 95,569 | ||||||
22 | .248 | .919 | 96,488 | ||||||
23 | .227 | .841 | 97,329 | ||||||
24 | .212 | .785 | 98,114 | ||||||
25 | .182 | .673 | 98,787 | ||||||
26 | .173 | .639 | 99,426 | ||||||
27 | .155 | .574 | 100,000 | ||||||
Extraction method: Principal component analysis PCA
Rotated factor matrix a
Factor | ||||||
1 | 2 | 3 | 4 | 5 | 6 | |
DL6 | .848 | |||||
DL5 | .745 | |||||
DL7 | .713 | |||||
DL8 | .709 | |||||
DL4 | .678 | |||||
DL3 | .598 | |||||
DL9 | .588 | |||||
DL10 | .579 | |||||
DL1 | .517 | |||||
CQ3 | .877 | |||||
CQ2 | .839 | |||||
CQ1 | .760 | |||||
CQ4 | .664 | |||||
TD3 | .712 | |||||
TD2 | .709 | |||||
TD1 | .683 | |||||
TD5 | .519 | |||||
TD4 | .500 | |||||
KN2 | .790 | |||||
KN1 | .771 | |||||
KN3 | .720 | |||||
VC2 | .832 | |||||
VC1 | .798 | |||||
VC3 | .786 | |||||
TD7 | .761 | |||||
TD6 | .692 | |||||
TD8 | .632 | |||||
Extraction method: Principal component analysis PCA. Rotation method: Varimax.
a. The rotation converges in 7 iterations.
KMO and Bartlett test
The Kaiser-Meyer-Olkin coefficient measures the goodness of fit of the sample. | .700 | |
Bartlett test | Chi-square value | 344,161 |
df | 3 | |
Sig. | .000 | |
Total variance explained
Ingredient
Initial Eigenvalues | Sum of squares of load factors after extraction | |||||
Total | % variance | Cumulative % | Total | % variance | Cumulative % | |
1 | 2,182 | 72,746 | 72,746 | 2,182 | 72,746 | 72,746 |
2 | .483 | 16,112 | 88,857 | |||
3 | .334 | 11,143 | 100,000 | |||
Extraction method: Principal component analysis PCA
Factor matrix a
Factor | |
1 | |
DD2 | .884 |
DD1 | .850 |
DD3 | .823 |
Extraction method: Principal component analysis PCA.
a. 1 factor extracted
Rotated factor matrix a
a. Only one factor is extracted. The solution cannot be rotated. |
Correlation coefficient
DUDINH | THAIDO1 | THAIDO2 | CHUANCQ | KHANANG | DONGLUC | VATCHAT | ||
DUDINH | Pearson correlation coefficient | 1 | .346 ** | .397 ** | .499 ** | .546 ** | .521 ** | -.171 ** |
Sig. (2 sides) | .000 | .000 | .000 | .000 | .000 | .002 | ||
N | 333 | 333 | 333 | 333 | 333 | 333 | 333 | |
THAIDO1 | Pearson correlation coefficient | .346 ** | 1 | .612 ** | .340 ** | .315 ** | .477 ** | -.259 ** |
Sig. (2 sides) | .000 | .000 | .000 | .000 | .000 | .000 | ||
N | 333 | 333 | 333 | 333 | 333 | 333 | 333 | |
THAIDO2 | Pearson correlation coefficient | .397 ** | .612 ** | 1 | .333 ** | .350 ** | .504 ** | -.156 ** |
Sig. (2 sides) | .000 | .000 | .000 | .000 | .000 | .004 | ||
N | 333 | 333 | 333 | 333 | 333 | 333 | 333 | |
CHUANCQ | Pearson correlation coefficient | .499 ** | .340 ** | .333 ** | 1 | .511 ** | .543 ** | -.124 * |
Sig. (2 sides) | .000 | .000 | .000 | .000 | .000 | .023 | ||
N | 333 | 333 | 333 | 333 | 333 | 333 | 333 | |
KHANANG | Pearson correlation coefficient | .546 ** | .315 ** | .350 ** | .511 ** | 1 | .455 ** | -.076 |
Sig. (2 sides) | .000 | .000 | .000 | .000 | .000 | .166 | ||
N | 333 | 333 | 333 | 333 | 333 | 333 | 333 | |
DONGLUC | Pearson correlation coefficient | .521 ** | .477 ** | .504 ** | .543 ** | .455 ** | 1 | -.097 |
Sig. (2 sides) | .000 | .000 | .000 | .000 | .000 | .077 | ||
N | 333 | 333 | 333 | 333 | 333 | 333 | 333 | |
VATCHAT | Pearson correlation coefficient | -.171 ** | -.259 ** | -.156 ** | -.124 * | -.076 | -.097 | 1 |
Sig. (2 sides) | .002 | .000 | .004 | .023 | .166 | .077 | ||
N | 333 | 333 | 333 | 333 | 333 | 333 | 333 |
**. Correlation is significant at the 0.01 significance level (2-tailed).
*. Correlation is significant at the 0.05 significance level (2-sided).
Appendix 9A: Regression results 1
Model summary b
Model
R | R 2 | R 2 correction | Standard error of forecast | Durbin-Watson test | |
1 | .655a | .430 | .419 | .56043 | 1,887 |
a. Independent variables: (Constant), THAIDO2, VATCHAT, CHUANCQ, KHANANG,
THAIDO1, DONGLUC
b. Dependent variable: DUDINH
ANOVA a
Model
Sum of squared differences | df | Mean square difference | F | Sig. | ||
1 | Regression | 77,135 | 6 | 12,856 | 40,931 | .000 b |
Error | 102,391 | 326 | .314 | |||
Total | 179,526 | 332 |
a. Dependent variable: DUDINH
b. Independent variables: (Constant), THAIDO2, VATCHAT, CHUANCQ, KHANANG, THAIDO1, DONGLUC
The coefficients a
Model
Unstandardized coefficient | Standardized coefficient | t | Sig. | Measure Multicollinearity | ||||
B | Standard error | Beta | Acceptability of the variable (Tolerance) | Variance magnification factor VIF | ||||
1 | (Constant) | .555 | .292 | 1.901 | .058 | |||
CHUANCQ | .172 | .054 | .171 | 3.203 | .001 | .612 | 1,634 | |
KHANANG | .316 | .051 | .313 | 6.181 | .000 | .680 | 1,470 | |
DONGLUC | .263 | .066 | .226 | 4.010 | .000 | .550 | 1,818 | |
VATCHAT | -.076 | .037 | -.089 | -2.049 | .041 | .928 | 1,077 | |
THAIDO2 | .105 | .055 | .108 | 1,924 | .055 | .560 | 1,785 | |
THAIDO1 | -.009 | .064 | -.008 | -.135 | .893 | .558 | 1,791 | |
a. Dependent variable: DUDINH