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1 Modeling Prior to select any models that could be applied to your project, we need firstly to gain full understanding of the following steps by steps:

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Presentation on theme: "1 Modeling Prior to select any models that could be applied to your project, we need firstly to gain full understanding of the following steps by steps:"— Presentation transcript:

1 1 Modeling Prior to select any models that could be applied to your project, we need firstly to gain full understanding of the following steps by steps: 1.Stating clearly of a)ObjectiveObjective b)Model drawingModel drawing c)HypothesesHypotheses 2.Decision variablesDecision variables 3.Types of dataTypes of data 4.Visibility, validity, reliability of data collectedVisibility, validity, reliability of data collected 5.Model selectionModel selection (to p2) (to p4) (to p5) (to p6) (to p9) (to p12) (to p28)

2 2 Stating objective clearly Points to remember! we need to establish a research rationale for your BBA research project that is, what is the problem and why does it call for your attention to carry out to this study! ExamplesExamples: (to p1) (to p3)

3 3 Ojective Reason(s) (to p2)

4 4 Model drawing Can we attempt to draw out a picture as to how our problem is to be studied? Example: (to p1)

5 5 Stating hypotheses State your model in a format of hypotheses example: (to p1)

6 6 Decision Variables What is it? A group of symbols use to represent each item of data we collect that describing the behavior of our subject Why do we need it? They render an easy way for us to –Representing our model of study –draw our model –construct a database Types of decision variables (to p1) (to p7)

7 7 Types of decision variables Two basic types of decision variables: 1.Independent variables 2.Dependent variables ( observations to be predicted ) General Format Y = X 1 + b A linear equation - Single variate equation (to p8)

8 8 Types of decision variables Depends on the type of problems, sometimes we do not clearly cluster them into dep or indep variables eg This relationship is normally known as multivariate analysis In general, in using the dependence technique term, we still treat: y1 + y2 = x1 + x2 + x3 Left hand side as dependent variables, Predictor variables Right hand side as independent variables, Explained variables (to p6)

9 9 Types of data Two types: 1.Metric scale ie quantitative/numerical values; different amount of degree 2. Non-metric scale ie. qualitative/categorical events that describe a subject Examples Question: Why do we need to classify these data accordingly? Answer! Answer! What about open-ended question? (to p10) (to p11) (to p1)

10 10 Metric scale Non-metric scale (to p9)

11 11 Types of data We need to know how to represent them when forming our database Example 1. We use values 1, 2, 3, 4, 5 to represent each marked value 2. We use value “0” to represent male and “1” for female “1” for yes, and “0” for no in religious etc. (to p9)

12 12 Visibility, Validity, Reliability of data collected It refers to the demonstration on: What data have you collected How countable were data being collected Typically, this part refers to the statistical behavior of those data collected Evidences of data quality (to p1) (to p13)

13 13 Evidences of data quality Few standardized data descriptions and data testing for your research output: 1.Representation of decision variablesRepresentation of decision variables 2.Sample characteristics and mean valuesSample characteristics and mean values 3.ReliabilityReliability 4.convergent validityconvergent validity 5.content validitycontent validity 6.Others include: discriminant validity, sample generalizibility, criterion related validity (to p12) (to p14) (to p15) (to p20) (to p23) (to p27)

14 14 Representation of decision variables Sometimes, it is advised to show how your model variables are being structured and coded, both in your model and dbase Example: Metric Data Non-Metric Data (to p13)

15 15 Sample characteristics and mean values This section is to describe the demographic data such as: a)position of respondents, their working experience, type of companies, size of companies;position of respondents, their working experience, type of companies, size of companies b)Gender and educational experience;Gender and educational experience c) Mean values of event.Mean values of event d)General observations say on internet experienceGeneral observations say on internet experience Note: Information presented here are very much depended on your research topic, questionnaire design! (to p13) (to p16) (to p18) (to p17) (to p19)

16 16 Survey deals with different practices of company types (to p15)

17 17 Survey deals with religious belief of managers (to p15)

18 18 Mean values This section describes mean values of your decision variables such as: Decision Variables (to p15)

19 19 Total Obs. Percentages Survey deals with Internet usage (to p15)

20 20 Reliability A reliability is referred to the study of the degree of consistency (ie. stability and consistency) of a measurement scale example: How to measure it?How to measure it Measurement items of a decision variable (to p21)

21 21  values Measure item is –Cronbach ’ s alpha (  ) coefficient; is the most popular method using to assess reliability –A high  value (close to 1) of a corresponding factor represents its high reliability. –there is not any universal rule to judge the acceptability and strength of the  value, Kerlinger (1973) suggested a minimum alpha value of 0.4. In the SPSS procedure of RELIABILITY ANALYSIS provides the alpha values Kerlinger, F. N. Foundation of Behavioral Research, 2 nd Edition, New York: Rinehard & Winston, 1973. (to p22)

22 22 Reliability Display of its data Please pay attention to the SPSS tutorial for obtaining this computer output (to p13)

23 23 Convergent validity Convergent validity is referred to the measurement of the degree of all measurement items of a factor are actually loaded onto a single variable. Convergent validity of factors can be assessed by the within-scale factor analysis. The procedure FACTOR ANALYSIS of SPSS is typically used to obtain its factor loadings. Our objective here is to ensure that all measurement items of a decision variable is loaded onto itself Example: All measures are onto a single variableare onto All measures not onto a single variablenot onto (to p13) (to p25) (to p24)

24 24 Convergent validity (example) Factor loadings that loaded onto itself Here, usually have only small values (to p23)

25 25 Factors loadings that do not load onto itself How to remedy itHow to remedy it? (to p26)

26 26 Convergent validity Remedial action –for those measurement items do not load onto itself –remove those items that share by more than one decision variable, and run the test again Example: (to p23)

27 27 Content validity The content validity of the instrument is assessed in this section. Content validity test is referred to the extent to which the measurement items of a factor are actually representing the meaning of that factor (Babbie 1992). Content validity test, rather than proven by statistical testing, is subjectively judged by researchers. Babbie, E. The Practice of Social Research, 6th Ed., Wadsworth, Belmont, CA., 1992. (to p13)

28 28 Model selection (cont.) There is no perfect answer as to tell which model is the best for your research Suggestion: –Check literature to see if any similar models/models can be used to solve your research objective: 1.Directly ( ie. similar application from other discipline ) 2.Indirectly ( ie with modification from methodology ) (to p29)

29 29 Model selection (cont.) In this subject, we show you how to apply the following statistically methods: 1.ANOVA 2.MANOVA 3.FACTOR ANALYSIS 4.DISCRIMINANT ANALYSIS And may be 1.CANONICAL ANALYSIS 2.PATH ANALYSIS

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