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General Information

Course ID (CB01A and CB01B)
PSYCD015.
Course Title (CB02)
Statistics and Research Methods in Social Science
Course Credit Status
Credit - Degree Applicable
Effective Term
Fall 2023
Course Description
This course examines elementary statistics including measures of central tendency, variability, probability, correlation, tests of significance, and hypothesis testing.
Faculty Requirements
Course Family
Not Applicable

Course Justification

This course is transferable to both the UC and the CSU systems, along with private universities, and can be used as an elective class in the social science area for both CSU and IGETC. It belongs on the Liberal Arts AA degree. This course provides students with a comprehensive understanding of statistics in the Social Sciences and is designed to give students a strong conceptual and computational experience in the application of statistics for psychologists, sociologists, and the broader social sciences fields.

Foothill Equivalency

Does the course have a Foothill equivalent?
Yes
Foothill Course ID
SOC F007.

Course Philosophy

Formerly Statement

Course Development Options

Basic Skill Status (CB08)
Course is not a basic skills course.
Grade Options
  • Letter Grade
  • Pass/No Pass
Repeat Limit
0

Transferability & Gen. Ed. Options

Transferability
Transferable to both UC and CSU
CSU GEArea(s)StatusDetails
CGB4CSU GE Area B4 - Mathematics/Quantitative ReasoningApproved
IGETCArea(s)StatusDetails
IG2XIGETC Area 2 - Mathematical Concepts and Quantitative ReasoningApproved
C-IDArea(s)StatusDetails
SOCISociologyApprovedC-ID SOCI 125

Units and Hours

Summary

Minimum Credit Units
4.0
Maximum Credit Units
4.0

Weekly Student Hours

TypeIn ClassOut of Class
Lecture Hours4.08.0
Laboratory Hours0.00.0

Course Student Hours

Course Duration (Weeks)
12.0
Hours per unit divisor
36.0
Course In-Class (Contact) Hours
Lecture
48.0
Laboratory
0.0
Total
48.0
Course Out-of-Class Hours
Lecture
96.0
Laboratory
0.0
NA
0.0
Total
96.0

Prerequisite(s)

PSYC D001. or SOC D001.; and Intermediate algebra or equivalent (or higher), or appropriate placement beyond intermediate algebra

Corequisite(s)

Advisory(ies)

EWRT D001A or EWRT D01AH or ESL D005.

Limitation(s) on Enrollment

(Not open to students with credit in the cross-listed course(s).)

(Also listed as SOC D015.)

Entrance Skill(s)

General Course Statement(s)

(See general education pages for the requirements this course meets.)

Methods of Instruction

Lecture and visual aids

Discussion of assigned reading

Discussion and problem solving performed in class

In-class exploration of Internet sites

Homework and extended projects

Laboratory experience which involve students in formal exercises of data collection and analysis

Quiz and examination review performed in class

Collaborative learning and small group exercises

Assignments

  1. Required reading from the text
  2. Perform the various calculations associated with each statistical test both in class and as homework
  3. Demonstrate a practical working knowledge of computer-based computation and data analysis, primarily via GoogleSheets-Excel and/or a statistical calculator.

Methods of Evaluation

  1. Midterm exams, weekly quizzes, and final examination provide the principal basis of evaluation. A minimum of two one hour examinations composed of both computational and concept based questions which will require the student to demonstrate ability in integrating the methods, ideas and techniques learned in class. Questions may also require the student to communicate ideas and conclusions in short essay format.
  2. Evaluate in-class calculation ability both through homework and group and individual demonstration. A minimum of three technology based projects/activities that make use of graphing calculators or computers addressing randomness, variation, and simulation will be evaluated for accuracy, completeness, and proper use of techniques and methods discussed in class. Questions may also require the student to communicate ideas and conclusions in short essay format.
  3. Evaluate competency with standard computer based statistics packages and general conceptual proficiency using multiple choice items and collection of sample coding and statistical output.

Essential Student Materials/Essential College Facilities

Essential Student Materials: 
  • None.
Essential College Facilities:
  • None.

Examples of Primary Texts and References

AuthorTitlePublisherDate/EditionISBN
Levin, J. A., Fox, J. A., & Forde, D. R. "Elementary Statistics in Social Research", 12th Ed., 2017 London: Pearson.

Examples of Supporting Texts and References

AuthorTitlePublisher
Campbell, D. T. and Stanley. "Experimental and Ouasi-experimental Designs for Research". Rand McNally, 1966.
Edwards, A. "Experimental Design in Psychological Research". 5th ed. New York: Holt, Rhinehart and Winston, 1985.
Everitt, Brian, S. and Wykes, Til. "A Dictionary of Statistics for Psychologists". New York: Holt, Rinehart and Winston, 2010.
Freedman, David, Pisani, Robert, and Purves, Rodger. "Statistics". 4th ed. Norton, W.W. & Company, 2007.
Gravetter, F. J., & Wallnau, F. B. "Statistics for the Behavioral Sciences" (9th Ed.) Belmont, CA: Wadsworth, 2012.
Kvam, Paul, H. and Vidakovic, Brani. "Nonparametric Statistics with Applications to Science and Engineering". Wiley, John & Sons, 2007.
Meltzoff, Julian. Critical Thinking About Research: Psychology and Related Fields. A.P.A. 1999.
Illowsky, B, & Dean, S. "Introductory Statistics." Houston: Openstaxcollege.org, 2016.
Rosenberg, K.M. "Statistics for Behavioral Sciences". Wm. C. Brown, 1990.
Salkind, N. J. Statistics for People who (Think They) Hate Statistics (6th Ed.). Thousand Oaks, CA: Sage Publications, 2016.
Stanovich, K. E. "How to Think Straight about Psychology" (11th Ed.). New York: Pearson, 2018.

Learning Outcomes and Objectives

Course Objectives

  • Define and explain the fundamental concepts of descriptive and inferential statistics.
  • Describe the major assumptions that characterize the scientific method inherent in various ways of analyzing data
  • Identify the measurement concepts appropriate for the different types of research data
  • Graph and interpret the basic types of frequency distributions
  • Describe, calculate, and explain the basic measures of central tendency and variability
  • Identify and explain the properties of the normal curve and standard scores
  • Calculate and apply linear regression equations to appropriate data
  • Calculate and apply the appropriate correlation formula to selected data
  • Explain and apply random sampling and probability concepts
  • Describe the various techniques used in hypothesis testing
  • Explain concept of power and apply this analysis to various statistical tests
  • Distinguish and explain the several types of sampling distributions
  • Explain and perform the calculations required in Student's T test
  • Explain and apply the analysis of variance techniques to appropriate data
  • Describe and explain the basic concepts that underlie Chi-square and other nonparametric tests

CSLOs

  • Demonstrate and explain the fundamental concepts of descriptive and inferential statistics as well as the major assumptions and methods of scientific analysis.

  • Describe and demonstrate various measurement concepts appropriate to different types of research data.

  • Graph and interpret basic frequency distributions,calculate and explain measures of central tendency and variablity.

  • Describe the basic properties of the normal curve and standard scores.

  • Calculate and apply linear regression, correlation, random sampling and probability analysis.

  • Describe various methods of hypothesis testing, from the three primary varieties of Student's T-test to analysis of variance.

Outline

  1. Define and explain the fundamental concepts of descriptive and inferential statistics.
    1. Numerical data display
    2. Central tendency
    3. Statistical dispersion
    4. Samples and populations
    5. Parametric and non-parametric statistics
    6. Hypothesis testing
  2. Describe the major assumptions that characterize the scientific method inherent in various ways of analyzing data
    1. Methods of knowing
    2. An overall example
    3. Scientific research
    4. Random sampling
    5. Descriptive and inferential methods of data analysis
  3. Identify the measurement concepts appropriate for the different types of research data
    1. Symbols and notation
    2. Measurement scales
    3. Nominal scales
    4. Ordinal scales
    5. Interval scales
    6. Ratio scales
    7. Continuous and discrete variables
    8. Real limits of a continuous variable
  4. Graph and interpret the basic types of frequency distributions
    1. Grouping scores
    2. Constructing a frequency distribution of grouped scores
    3. Relative frequency, cumulative frequency, and cumulative percentage distributions
    4. Percentiles and percentile rank
    5. Computation of percentile rank
    6. Graphing frequency distributions
    7. The bar graph
    8. The histogram
    9. The frequency polygon
    10. The cumulative percentage polygon
  5. Describe, calculate, and explain the basic measures of central tendency and variability
    1. Measures of central tendency
    2. The arithmetic mean
    3. The median
    4. The mode
    5. Measures of variability
    6. The range
    7. Deviation scores
    8. The standard deviation
    9. The variance
  6. Identify and explain the properties of the normal curve and standard scores
    1. The normal curve
    2. Area contained under the normal curve
    3. Standard scores
    4. Characteristics of z scores
    5. Finding areas corresponding to any raw score
    6. Finding the raw score corresponding to a given area
  7. Calculate and apply linear regression equations to appropriate data
    1. Linear relationships
    2. Deriving the equation of the straight line
    3. Constructing the least-squares regression line
    4. Regression of X on Y
    5. Measuring prediction errors: the standard error of estimate
  8. Calculate and apply the appropriate correlation formula to selected data
    1. The concept of correlation
    2. The linear correlation coefficient Pearson
    3. The Spearman rank order correlation coefficient rho
    4. Effect of range on correlation
    5. Correlation and causation
  9. Explain and apply random sampling and probability concepts
    1. Random sampling
    2. Techniques for random sampling
    3. Sampling with or without replacement
    4. Probability
    5. Computing probability
  10. Describe the various techniques used in hypothesis testing
    1. Type I and Type II errors
    2. Alpha level and the decision process
    3. Evaluating the tail of the distribution
    4. One- and two-tailed probability evaluations
  11. Explain concept of power and apply this analysis to various statistical tests
    1. Calculation of power
    2. Power and beta
  12. Distinguish and explain the several types of sampling distributions
    1. Rules of probability
    2. Laws of expected value and variance
    3. Central limit theorem
    4. Sampling distribution of the mean
  13. Explain and perform the calculations required in Student's T test
    1. Comparison of the z and t tests
    2. The sampling distribution of t
    3. Degrees of freedom
  14. Explain and apply the analysis of variance techniques to appropriate data
    1. Introduction: the F distribution
    2. The analysis of variance (ANOVA)
    3. Overview of the one-way analysis of variance technique
    4. Within-groups variance estimate
    5. Between-groups variance estimate
    6. The F ratio
    7. Review of the logic underlying the one-way ANOVA
    8. The relationship between the analysis of variance and the t test
    9. Assumptions underlying the analysis of variance
    10. Two-way analysis of variance
  15. Describe and explain the basic concepts that underlie Chi-square and other nonparametric tests
    1. Introduction: distinction between parametric and nonparametric tests
    2. Chi-square (x2) single-variable experiments
    3. Chi-square and contingency tables
    4. Mann-Whitney U test
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