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TEACHING > SOCIAL STATISTICS

 

The course introduces basic concepts of statistical analysis, both in theory (lectures) and practice (labs). The course begins with a discussion of descriptive statistics, including frequency distributions, graphs, and measures of central tendency and variability. Next, the course examines relationships between variables and measures of association, including bivariate regression and correlations. The course concludes with an introduction to inferential statistics, including t-tests, chi-square, ANOVA, and ordinary least squares regression.

 

Syllabus

Learning Objectives

  • To become an educated consumer of statistical information
  • To apply what is learned in this course to deal with statistical information presented in daily life and in their academic field
  • To be able to analyze and discern the uses and abuses of statistics.

To download a syllabus with class policies click here.

Descriptive Statistics

  • Week 1: The What and Why of Statistics
  • Week 2: Frequency Tables and Graphics
  • Week 3: Measures of Central Tendency
  • Week 4: Measures of Variability
  • Week 5: Exam 1

Inferential Statistics

Required Text and Materials

  • Linneman, Thomas J.  2014. Social Statistics: Managing Data, Conducting Analyses, Presenting Results. Second Edition. New York: Routledge. ISBN: 9780415661478
  • Scientific Calculator (with square root function)

Recommended Readings

  • Frankfort-Nachmias, Chava and Anna Leon-Guerrero. 2015. Social Statistics for a Diverse Society. Thousand Oaks, CA: Sage Publications, Inc.
  • Salkind, Neil. 2013. Statistics for People Who (Think They) Hate Statistics. Fifth edition. Sage Publications. ISBN: 9781452277714
  • Gonick, L., and W. Smith. 1993. Cartoon Guide to Statistics. New York: HarperCollins.
  • Huff, D. 1954. How to Lie with Statistics. New York: W. W. Norton & Company.

 

Sample Assignments

I use a combination of interactive practices exercises, traditional homework assignments, SPSS assignments, quizzes, and exams in this course. You can see examples of some of these assignments by clicking the assignment types listed below.

Introductory Concepts

Measures of Central Tendency

Measures of Variability

Relationships Between Variables

 

Course Modules

There is a course website with a page for each week that contains links to lecture videos and slides, assignments, exams, and additional resources.

In the first week of class we will:

  • Get acquainted
  • Review the syllabus and course requirements
  • Learn how to access SPSS in the virtual lab

Problem: What are the different types of data and statistics?

Reading: Frankfort-Nachmias Chapter 1

Learning Objectives: After completing this module students should be able to:

  • Understand the research process.
  • Identify and distinguish between independent and dependent variables.
  • Identify and distinguish between three levels of measurement.
  • Understand descriptive versus inferential statistical procedures.

Practice Exercises:

  • Interactive:

Assignments:

Problem: Can we trust our statistics?

Reading: Salkind Chapter 6

Learning Objectives: After completing this module students should be able to:

  • Explain what reliability and validity are and why they are important.
  • Understand why measurement is important.
  • Compute and interpret various types of reliability coefficients.
  • Compute and interpret various types of validity coefficients.

Assignments:

  • Homework:
  • Lab:

Problem: How can we organize our data?

Reading: Frankfort-Nachmias Chapter 2

  • Chapter Outline:

Learning Objectives: After completing this module students should be able to:

  • Understand how to construct and analyze frequency, percentage and cumulative distributions.
  • Understand how to calculate proportions and percentages.
  • Recognize the differences in frequency distributions for nominal, ordinal and interval-ratio variables.
  • Read statistical tables in research literature

Practice Exercises:

Assignments:

  • Homework: Frequency Tables
  • Lab: The lab for this module is combined with the lab for Module V. Graphing Data

Problem: How can we present our data in a way that anyone can make sense of it?

Reading: Linneman Chapter 2
Optional: Frankfort-Nachmias Chapter 3 and Data Points Chapter 3

  • Chapter Outline:

Learning Objectives: After completing this module students should be able to:

  • Construct and interpret a pie chart, bar graph, histogram, line graph, and time-series chart.
  • Choose appropriate graphs for one, two, and three variables.
  • Analyze and interpret charts and graphs in the literature.
  • Understand the ways graphs can lie, and be able to spot such lies.

Assignments:

Problem: How can we describe the average, or typical, value that represents all of the values in our data?

Reading: Linneman Chapter 3, pages 92-102

  • Chapter Outline:

Learning Objectives: After completing this module students should be able to:

  • Define all measures of central tendency and explain their differences, relative strenghts and weaknesses.
  • Determine the mode in a given distribution.
  • Find or calculate the median and percentiles.
  • Calculate the mean.
  • Determine the shape of a distribution.

Practice Exercises:

Assignments:

  • Homework: Central Tendency
  • Lab: The lab for this module is combined with the lab for Module V. Graphing Data

Problem: How can we describe the diversity in our data?

Reading: Linneman Chapter 3, pages 102–128

  • Chapter Outline:

Learning Objectives: After completing this module students should be able to:

  • Understand the importance of variability.
  • Learn how to calculate the index of qualitative variation (IQV), range, interquartile range (IQR), the variande, and the standard deviation.
  • Understand the criteria for choosing a measure of variation.

Practice Exercises:

Assignments:

Problem: How can we visualize the distribution of our data?

Reading: Salkind Chapter 8, pages 145-152

Learning Objectives: After completing this module students should be able to:

  • Recognize the importance and the use of the normal distribution in statistics.
  • Describe the properties of the normal distribution.

Assignments:

  • Lab: The lab for this module is combined with the lab for Module IX. Z-Scores

Problem: How can we tell when a value for one of our variables is rare?

Reading: Salkind Chapter 8, pages 152-166

Learning Objectives: After completing this module students should be able to:

  • Transform a raw score into a standard score (z-score) and vice versa.
  • Use the standard normal table.
  • Transform a z-score into proportion (or percentage) and vice versa.
  • Find and explain the percentile rank of a score.

Practice Exercises:

  • Interactive:
  • Standard:

Assignments:

Problem:

Reading:

  • Chapter Outline:

Learning Objectives: After completing this module students should be able to:

  • Understand...

Practice Exercises:

  • Interactive:
  • Standard:

Assignments:

  • Homework:
  • Lab:
  • Crossword Puzzle:
  • Video:
  • Link:

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© Yvette Young. All Rights Reserved. Version Date 2016.