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MGS 8150MGS 8150Causal Model – extraCausal Model – extra

Dr. Subhashish (Sub) SamaddarDr. Subhashish (Sub) SamaddarGeorgia State UniversityGeorgia State University

J. Mack Robinson College of BusinessJ. Mack Robinson College of BusinessExecutive EducationExecutive EducationAtlanta, GA 30303Atlanta, GA 30303

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Causal Model: Some Useful TipsCausal Model: Some Useful Tips

Choose and reason your Dependent Variable Y and Choose and reason your Dependent Variable Y and Independent variable (X) carefully. Be able to Independent variable (X) carefully. Be able to reason: A change in X should cause a change in Y reason: A change in X should cause a change in Y AND a change in Y should not cause a change in X.AND a change in Y should not cause a change in X.

Your data for Y should have variance – no variance Your data for Y should have variance – no variance is bad. is bad.

Your data for each X variable should have variance Your data for each X variable should have variance – no variance is bad.– no variance is bad.

Recognize variable types that you are dealing with Recognize variable types that you are dealing with and take appropriate action: and take appropriate action: • FourFour

• NominalNominal• OrdinalOrdinal• Interval/ RatioInterval/ Ratio

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Causal Model: Role of Variable Types Causal Model: Role of Variable Types

Not all variables created equal! Based Not all variables created equal! Based on amount of information contained in on amount of information contained in the data (or variable)the data (or variable)

Why do we care – to be able to use Why do we care – to be able to use them appropriately in causal modelingthem appropriately in causal modeling

How many different types of variables? How many different types of variables? •FourFour

•NominalNominal•OrdinalOrdinal•Interval/ RatioInterval/ Ratio

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Scales of MeasurementScales of Measurement Nominal – data contains only name or label to Nominal – data contains only name or label to

describe an attribute; can be numeric or non-describe an attribute; can be numeric or non-numeric. numeric. Example: Example:

University students data can use a University students data can use a nonnumeric label such as Business, nonnumeric label such as Business, Humanities, Education, and so on.Humanities, Education, and so on.

Gender – male/ female.Gender – male/ female. How to model this type of data:How to model this type of data:

Use dummy variable; easy for two values Use dummy variable; easy for two values such as Gender – Use dummy variable X1 such as Gender – Use dummy variable X1 where X1 = 0 means female, X1 = 1 means where X1 = 0 means female, X1 = 1 means male. If you have more than two values talk male. If you have more than two values talk to Sub. to Sub.

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Scales of MeasurementScales of Measurement Ordinal – nominal data properties plus there Ordinal – nominal data properties plus there

is a meaningful order or rank of the data; can is a meaningful order or rank of the data; can be numeric or non-numericbe numeric or non-numeric

Examples:Examples: 1. University students data can use a 1. University students data can use a

nonnumericnonnumeric label such as Freshman, Sophomore, Junior, label such as Freshman, Sophomore, Junior, or Senior.or Senior.

2. Military ranks …2. Military ranks …

How to model this type of data:How to model this type of data:

These can use numeric code … such as 1, 2, These can use numeric code … such as 1, 2, 3, 4 etc. where 2 represents something more 3, 4 etc. where 2 represents something more than 1 and so on.than 1 and so on.

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Scales of MeasurementScales of MeasurementInterval – ordinal data properties plus a fixed unit Interval – ordinal data properties plus a fixed unit

of measure expressing the interval between of measure expressing the interval between the observations; the observations; alwaysalways numeric. numeric.

Ratio – interval data properties plus ratio of two Ratio – interval data properties plus ratio of two values are meaningful. values are meaningful.

Examples:Examples: 1. (Interval data) John’s exam score is 87, 1. (Interval data) John’s exam score is 87,

Jane’s score is 94. Jane scored 7 points more Jane’s score is 94. Jane scored 7 points more than John.than John.

2. (Ratio data) Distance, Height, Weight, Time, 2. (Ratio data) Distance, Height, Weight, Time, Money …Money …

How to use them in causal model: How to use them in causal model: The The regression model that you can run in regression model that you can run in Excel has to have an Interval or ratio Excel has to have an Interval or ratio data (variable) as the dependent data (variable) as the dependent variable. X variables can be any type.variable. X variables can be any type.

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How much data do you need?How much data do you need?

Some rule of thumbs:Some rule of thumbs:1.1.Keeping it simple, it depends on how Keeping it simple, it depends on how

many X variables you have in your many X variables you have in your model.model.

2.2.Will discuss some rule-of-thumb in Will discuss some rule-of-thumb in class:class:

Use the largest of:Use the largest of:a. 50+8*k (for R-squared test only)a. 50+8*k (for R-squared test only)b. 104+k (for coefficients tests b. 104+k (for coefficients tests

only)only)Where k represents number of X Where k represents number of X

variables in your regression model.variables in your regression model.

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