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School of Education and Human Development University of Colorado Denver

School of Education and Human Development
 

Statistics Admissions Requirement


If you would like to apply to the PhD or EdD program but have not completed a basic or advanced statistics course with a grade of B or better in the last 5 years, you must instead complete one of the following two options:

Option 1

Take a basic statistics course and earn a grade of B or better (the course must be called "statistics" or have "statistics" in the title). The course may be completed at the graduate or undergraduate level at any accredited institution, including a community college.

Option 2

If, due to earlier academic training or recent professional responsibilities, you believe you have the necessary knowledge of statistics, you may demonstrate this knowledge through passing our basic statistics proficiency test. Please contact education@ucdenver.edu to schedule the test.

The test will cover the following material:

Distributions

  • Given a small data set, find the mean, median, and mode
  • Identify skew as positive or negative
  • Recognize the relative position of the mean and the median in a skewed distribution
  • Calculate the variance of 4 scores
  • Understand the relationship between variance and standard deviation
  • Meaning of percentile scores
  • Characteristics of normal distribution, including areas under the curve

Correlation

  • Estimate the magnitude and direction of a correlation from a scatterplot
  • Define positive and negative correlation
  • Explain why r2 represents the proportion of variance shared or explained by the correlation of two variables
  • Interpret the meaning of "high" and "low" correlation

Measurement

  • Identify examples of 4 levels of measurement (nominal, ordinal, etc.) and correctly categorize measures into these levels
  • Know the formula for z scores, and convert raw scores to z scores
  • Interpret z scores, including converting z scores to percentiles when scores are normally distributed

Causal relationships

  • Independent, dependent variable
  • Correlation and causation

Concepts of Inferential Statistics

  • Central Limit Theorem
  • Compute the standard error of the mean
  • Null hypothesis
  • Rejecting the null hypothesis
  • Confidence interval around sample mean
  • Alpha level
  • Type I error, Type II error
  • Random selection
  • Define statistical power
  • Identify ways to increase power

Inferential Tests

Know when to use

  • Chi Square
  • t-test for independent samples
  • t-test for dependent samples
  • ANOVA
  • Interpret SPSS printout of results from ANOVA
  • Interpret SPSS printout of results from t-test for independent samples
  • Understand the logic underlying ANOVA, F statistic

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Human Development

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