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EPSY 5210 Ed. Statistics Instructor: Hector Ponce Background: Research Interest Experience with Quantitative Analysis Additional comments.

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Presentation on theme: "EPSY 5210 Ed. Statistics Instructor: Hector Ponce Background: Research Interest Experience with Quantitative Analysis Additional comments."— Presentation transcript:

1 EPSY 5210 Ed. Statistics Instructor: Hector Ponce Background: Research Interest Experience with Quantitative Analysis Additional comments

2 In the Beginning Necessary Information

3 What’s statistics? The science of organizing and analyzing information. Inference To find: – Areas under the bell curve (e.g., z test) – Comparing means (i.e., t-test, and ANOVA) – Correlations (Pearson r) – Compare proportions (chi square)

4 Parameters/statistics Mean (average): Standard deviation (for a sample):

5 What’s statistically significant differences?

6 The normal curve distribution function:

7 Effect size: Cohen’s d, r, and r2 To what extent a phenomenon exists. Ratio: Cohen’s d Percentage: r and r2

8 Population and Samples Population: Comprises all members of a group – Quantitative values are parameters – Inferential statistics infer population characteristics from sample data – Notation is in Greek symbols Sample – Quantitative values are estimates – Descriptive statistics describe samples and do not infer or generalize to populations – Notation is in alphanumeric

9 Types of Statistics Parametric statistics (z-test, t-test, ANOVA ) – Meet certain theoretical assumptions Example - Variable is normally distributed in the population Data must be interval or ratio Non parametric statistics (Chi square) – Less rigorous theoretical assumptions Example - don’t meet normal distribution assumptions Example – distribution unknown or “free”

10 Statistical Values Constants: Values that don’t change – Example: Pi is a constant of 3.1214, diameter of the earth is 7,918 miles Variables: Values that are free to change – Example: Length or depth are variable – Example: An price assigned to a product Discrete: Value can only be whole numbers – Example: Family size

11 Statistical Values (con’t) Continuous: Value can range from negative infinite to positive infinite. Normally, the range is from “0” to some positive number – Example: Weight or height are continuous (97.3 lbs or 5’3.2”)

12 Measurement Four Scales – Qualitative Scales Nominal: Identification of substance – Gender – Ethnicity Ordinal: Ranks order of substance – In a competition: First place, second place. – Quantitative Scales Ratio: Absolute zero of substance: Kelvin – Speed – Weight Interval: Arbitrary zero: Celsius – GRE scores

13 Types of Measurements

14 Research Variables Independent Variables: Vary naturally or are manipulated by the research Dependent Variables: “Dependent” on the independent variable; outcome – Weight (dependent variables) dependent on caloric intake (independent variable)

15 References Dr. Young Dr. Roberts


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