ข้อสอบQ.E .ทดลองทำดู

Silpakorn University

1.       Describe technical terms as follow:-

a.       Inferential statistics

b.      Homogeneity sample

c.       Extraneous variables

d.      The solomon four-groups design

e.      Sample size for an experimental research

f.        Unobstrusive measures

g.       Pleacebo effect

h.      Infinite population

i.         µ and  б

j.        Confounding variable

2.       Explain the differences between two technical terms as follow:-

a.       Hypothesis and assumption

b.      Limitation and delimitation

c.       External validity and internal validity

d.      Parametric  statistics and non-parametric statistics

e.      The one-shot case study and the one-shot, non-experimental case study

3.       Under what circumstances would you select quantitative or qualitatuve research methodology for your dissertation? In the doctoral level, is it enough to do the research by only quantitative or qualitative methodoly? Give me your comment, please

4.       What is the main differences point between canonical correlation and path analysis in the statistical used for multivariate research methodology?  Can we use LISREL software for data analysis both for those statistics?

5.       If you used opinionnaire for data gathering, can Chi-square test of association appropriate for analysed the causal effect of your variable? In the other hand, how about the concordance or the differences? Clarify this issue

6.       Express your idea about  the should be doctoral dissertation, and what is the dissertation you decide for submit in partial fulfillment of the requirement for the degree Ph.D., tell more about your progression

A.      The following diagrams are Multivariate Dependence Methods: 1.       Canonical Correlation Y1 + Y2 + Y3+…… + Yn = X1 + X2 + X3+ ….. + Xn  (metric, nonmetric)       (metric, nonmetric) 2.       Multivariate Analysis of Variance Y1 + Y2 + Y3+…… + Yn = X1 + X2 + X3+ ….. + Xn (metric)                                  (nonmetric) 3.       Analysis of Variance Y1 = X1 + X2 + X3 + …….+ Xn (metric)        (nonmetric) 4.       Multiple Discriminant Analysis Y1 = X1 + X2 + X3 + …….+ Xn (nonmetric)         (metric) 5.       Multiple Regression Analysis Y1 = X1 + X2 + X3 + …….+ Xn (metric)       (nonmetric, metric) 6.       Conjoint Analysis Y1 = X1 + X2 + X3 + …….+ Xn (nonmetric, metric)       (nonmetric) B.      Metric is data also called quantitative, interval or ratio, these measurements identity or describe subjects (or objects) not only on the possession of an attribute but also by the amount or degree to which the subject may be characterized by the attribute. For example, a person’s age. Nonmetric is data also called qualitative, nominal , ordinal , these are characteristics, attributes or categorical properties that identify or describe a subject or object. They differ from Metric by indicating the presence of an attribute, but not the amount. For example, occupation.

 

ASK  From the above information, would you please explain your understanding of data analysis by the used of multivariate techniques.