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Copyright 2004 Dr. J. Kyle Roberts niversity of North Texas Unit 1: Background to Inferential Statistics Lesson 1: Levels of Scale EDER 6010: Statistics for Educational Research Dr. J. Kyle Roberts University of North Texas

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Page 1: Unit 1 lesson 1

Copyright 2004Dr. J. Kyle Roberts

University of North Texas

Unit 1: Background to Inferential Statistics

Lesson 1: Levels of Scale

EDER 6010: Statistics for Educational Research

Dr. J. Kyle Roberts

University of North Texas

Page 2: Unit 1 lesson 1

Copyright 2004Dr. J. Kyle Roberts

University of North Texas

What is Statistics?

•Making sense of data

Used with permission: www.jasonlove.com

•Synthesizing

•Inference

•STATISTICS IS NOT MATH!!!!

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Page 3: Unit 1 lesson 1

Copyright 2004Dr. J. Kyle Roberts

University of North Texas

Levels of Scale

Nominal

Ordinal

Interval

Ratio

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Page 4: Unit 1 lesson 1

Copyright 2004Dr. J. Kyle Roberts

University of North Texas

Nominal Scaling

•In nominal scaling, each of the values has a meaning.

•Each observation belongs to one mutually exclusive category and has no logical order.

•Examples:

Gender Ethnicity ProfessionGroup

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Page 5: Unit 1 lesson 1

Copyright 2004Dr. J. Kyle Roberts

University of North Texas

Ordinal Scaling

•In ordinal scaling, each of the values is in rank order.

•Each observation belongs to one mutually exclusive category, but we now have logical order.

•Examples:

Likert-type scalesLetter Grades

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Page 6: Unit 1 lesson 1

Copyright 2004Dr. J. Kyle Roberts

University of North Texas

Interval Scaling•In interval scaling, each of the values has a specific order that reflects equal differences.

•Each observation belongs to one mutually exclusive category, with logical order, and equal differences between each of the points.•Examples:

Temperature IQ Scores SAT/GRE Scores

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Page 7: Unit 1 lesson 1

Copyright 2004Dr. J. Kyle Roberts

University of North Texas

Ratio Scaling•In ratio scaling, each of the values has a specific order that reflects equal differences and a “true” zero.

•Each observation belongs to one mutually exclusive category, with logical order, equal differences between each of the points, and has a “true” zero.•Examples:

Kelvin Scale Height/Weight Speed

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Page 8: Unit 1 lesson 1

Copyright 2004Dr. J. Kyle Roberts

University of North Texas

Why so many levels?

Only interval and ratio scales can be submitted to math operations

Average gender???

Average ethnicity???

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Page 9: Unit 1 lesson 1

Copyright 2004Dr. J. Kyle Roberts

University of North Texas

A few practice questions

• Nominal

• Ordinal

• Interval

• Ratio

Which level of scale is implied by the following statement: “Jill’s score is three times greater than Eric’s score.”

• Nominal

• Ordinal

• Interval

• Ratio

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Page 10: Unit 1 lesson 1

Copyright 2004Dr. J. Kyle Roberts

University of North Texas

A few practice questions

• Nominal

• Ordinal

• Interval

• Ratio

Which level of scale is implied by the following statement: “Addie had the highest score.”

• Nominal

• Ordinal

• Interval

• Ratio

Next Slide

Page 11: Unit 1 lesson 1

Copyright 2004Dr. J. Kyle Roberts

University of North Texas

Unit 1: Background to Inferential Statistics

Lesson 1: Levels of Scale

EDER 6010: Statistics for Educational Research

Dr. J. Kyle Roberts

University of North Texas