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20/06/2020 06:08:00 AM

Research method to study factors affecting decision to choose the digital marketing course at Conado company- P2

1. Scale Encode

1.1. Training Program scale

TP1: Course content, objectives, clear output standards          

TP2: Appropriate course materials include curriculum and information      

TP3: The training program is updated regularly

TP4: The training program meets the professional development requirements of students           

1.2. Instructor scale

INS1: Instructors are enthusiastic in teaching    

INS2: Instructors have specialized knowledge  

INS3: Instructors are fun, friendly with students            James

INS4: Instructors care about students      

1.3. Cost scale

COST1: Reasonable cost   

COST2: The center has a cost waiver policy      

COST3: Center awarded scholarships

COST4: There a regime of flexible fee collection (cost)          

1.4. Facilities scale

FAC1: The classroom is cool        

FAC2: Audiovisual equipment meets the requirements           

FAC3: Class sizes are reasonable 

FAC4: Electronic databases (learning materials, research data, ...) serve well for learning           

1.5. Reputation scale

REP1: Famous brands        

REP2: Reputable in the industry  

REP3: There are famous instructors in the industry      

REP4: Commitment to output quality      

1.6 Decision Scale:

DEC1: I will continue to follow the next course

DEC2: I will introduce acquaintances to the course     

DEC3: I will register for the course          

 

2. SPSS Data Analysis Method

2.1. Reliability Test Cronbach’s Alpha

Cronbach’s Alpha, also known as Coefficient Alpha, was developed by author Lee Cronbach in 1952 with the purpose of measuring reliability, internal stability. The Cronbach’s Alpha coefficient tells us the reliability of each factor and allows us to evaluate if certain measures belong to a variable that are appropriate. Below is index to evaluate Cronbach’s Alpha:

0.9 ≤ α :          Excellent

0.8 ≤ α < 0.9: Good

0.7 ≤ α < 0.8: Acceptable

0.6 ≤ α < 0.7: Questionable

0.5 ≤ α < 0.6 : Poor

α < 0.5 :          Unacceptable

2.2. Exploratory Factor Analysis (EFA)

Exploratory Factor Analysis (EFA) aims to evaluate two important types of values of the scale: convergent and discriminant values.

The EFA factor analysis method belongs to an interdependence technique, which means that there are no independent and dependent variables, but it is based on the interrelationships.

According to Hair & ctg (1998, 111): Factor loading (factor load factor or factor weight) is the norm to ensure the real meaning level of EFA, Factor Loading is the target to ensure the theoretical significance level of EFA. Hair & ctg (1998,111) also recommends that, if the Factor Loading factor is selected> 0.3, the sample size should be at least 350, if the sample size is about 100, it should be selected Factor Loading> 0.55, if the sample size is about 50, the Factor Loading > 0.75.

The KMO index (Kaiser-Meyer-Olkin) is the index used to consider the suitability of factor analysis. A large KMO value means that factor analysis is appropriate. If the KMO index is priced at 0.5 ≤ KMO ≤ 1, factor analysis is appropriate.

Gerbing & Anderson: Results of factors analysis are acceptable when total variance extracted > 50% and Eigenvalue > 1. Percentage Of Variance > 50%. It shows percentage variation of observed variables.

Nunnally & Burnstein: Observed Variables has a correlation coefficient Item-Total Correlation < 0.3 will be eliminated and scale ensures reliability when Cronbach’s Alpha ≥ 0.6.

2.3. Correlation And Regression Analysis

Correlation Analysis in order to check the relationship between variables through Sig. If Sig < 0.05, there is a relationship between variables. Variables which have relation to the dependent variable will be selected to run Regression Model.

Regression Analysis in SPSS is the verification of research model after analyzing Cronbach’s Alpha, EFA, Correlation. This stage will choose variables that are qualified for Regression Analysis requirements. Regression Analysis is to determine  specifically the weight of each independent variables affecting dependent variables. Then regression equation will be selected.

+ Sig < 0.05: Variables will be qualified for research model

+ R-Square (Adjusted R-Square) is the level of accuracy or relevance of model. Normally, the model will be qualified when R-Square > 50%.

+ Standardized Beta Coefficients shows the relationship between independent variables and dependent variables or the level of variation of dependent variables when independent variables fluctuate.

+ ANOVA Analysis shows that if F parameter has Sig = 0, It indicates that regression model is built in accordance with data that is collected from survey process.

Nguyen Thi My My

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