1. 1 1 Slide MGS 8150 Causal Model – extra Dr. Subhashish (Sub) Samaddar Georgia State University J. Mack Robinson College of Business Executive Education Atlanta, GA 30303

2. 2 2 Slide Causal Model: Some Useful Tips n Choose and reason your Dependent Variable Y and Independent variable (X) carefully. Be able to reason: A change in X should cause a change in Y AND a change in Y should not cause a change in X. n Your data for Y should have variance – no variance is bad. n Your data for each X variable should have variance – no variance is bad. n Recognize variable types that you are dealing with and take appropriate action: Four Four NominalNominal OrdinalOrdinal Interval/ RatioInterval/ Ratio

3. 3 3 Slide Causal Model: Role of Variable Types n Not all variables created equal! Based on amount of information contained in the data (or variable) n Why do we care – to be able to use them appropriately in causal modeling n How many different types of variables? Four Four NominalNominal OrdinalOrdinal Interval/ RatioInterval/ Ratio

4. 4 4 Slide Scales of Measurement n Nominal – data contains only name or label to describe an attribute; can be numeric or non-numeric. n Example: n University students data can use a nonnumeric label such as Business, Humanities, Education, and so on. n Gender – male/ female. n How to model this type of data: n Use dummy variable; easy for two values such as Gender – Use dummy variable X1 where X1 = 0 means female, X1 = 1 means male. If you have more than two values talk to Sub.

5. 5 5 Slide Scales of Measurement n Ordinal – nominal data properties plus there is a meaningful order or rank of the data; can be numeric or non-numeric Examples: 1. University students data can use a nonnumeric 1. University students data can use a nonnumeric label such as Freshman, Sophomore, Junior, label such as Freshman, Sophomore, Junior, or Senior. 2. Military ranks … 2. Military ranks … How to model this type of data: These can use numeric code … such as 1, 2, 3, 4 etc. where 2 represents something more than 1 and so on. These can use numeric code … such as 1, 2, 3, 4 etc. where 2 represents something more than 1 and so on.

6. 6 6 Slide Scales of Measurement Interval – ordinal data properties plus a fixed unit of measure expressing the interval between the observations; always numeric. Ratio – interval data properties plus ratio of two values are meaningful. Examples: Examples: 1. (Interval data) John’s exam score is 87, Jane’s score is 94. Jane scored 7 points more than John. 1. (Interval data) John’s exam score is 87, Jane’s score is 94. Jane scored 7 points more than John. 2. (Ratio data) Distance, Height, Weight, Time, Money … 2. (Ratio data) Distance, Height, Weight, Time, Money … How to use them in causal model: The regression model that you can run in Excel has to have an Interval or ratio data (variable) as the dependent variable. X variables can be any type.

7. 7 7 Slide How much data do you need? Some rule of thumbs: 1.Keeping it simple, it depends on how many X variables you have in your model. 2.Will discuss some rule-of-thumb in class: Use the largest of: a. 50+8*k (for R-squared test only) b. 104+k (for coefficients tests only) Where k represents number of X variables in your regression model.

8. 8 8 Slide