# SOC 7: STATISTICS FOR THE BEHAVIORAL SCIENCES

## Foothill College Course Outline of Record

Heading | Value |
---|---|

Effective Term: |
Summer 2023 |

Units: |
5 |

Hours: |
5 lecture per week (60 total per quarter) |

Prerequisite: |
One of the following: PSYC 1, 1H, SOC 1, 1H; Intermediate Algebra or equivalent. |

Advisory: |
UC will grant transfer credit for a maximum of one course from the following: PSYC 7, SOC 7, MATH 10 or 17—students are strongly encouraged to meet with a counselor for appropriate course selection; not open to students with credit in PSYC 7. |

Degree & Credit Status: |
Degree-Applicable Credit Course |

Foothill GE: |
Area V: Communication & Analytical Thinking |

Transferable: |
CSU/UC |

Grade Type: |
Letter Grade (Request for Pass/No Pass) |

Repeatability: |
Not Repeatable |

Cross-Listed: |
PSYC 7 |

## Student Learning Outcomes

- Using appropriate descriptive and inferential statistics, students will be able to analyze and perform computations on data sets.
- Students will be able to accurately match and perform the appropriate statistical tests for a wide range of descriptive, correlational, qualitative, and experimental research designs.

## Description

## Course Objectives

The student will be able to:

- Distinguish among different scales of measurement and their implications
- Identify the standard methods of obtaining data and identify advantages and disadvantages of different sampling techniques and techniques for obtaining data
- Interpret data displayed in tables and graphs
- Determine measures of central tendency and variability for a given data set
- Calculate measures of central tendency and variability for discrete distributions
- Apply concepts of sample space and probability
- Calculate probabilities using normal and t-distributions
- Explain the difference between sample and population distributions and the role played by central limit theorem
- Calculate z-scores
- Construct standardized distributions from z-scores
- Construct and interpret confidence intervals
- Explain the basic concept of hypothesis testing, including Type I and II errors
- Interpret levels of statistical significance, including p-values
- Compute by hand and interpret data from many types of statistical tests, including the z-test, the single-sample t-test, the independent samples t-test, the repeated measures t-test, one-way ANOVA, pearson's correlation, spearman's correlation, linear regression, and chi-square
- Compute using software, such as SPSS, Excel, or R, and interpret data from many types of statistical tests, including the z-test, the single-sample t-test, the independent samples t-test, the repeated measures t-test, one-way ANOVA, pearson's correlation, spearman's correlation, linear regression, and chi-square
- Interpret the output of a computer-based statistical analysis from programs, such as SPSS, Excel, or R
- Formulate a hypothesis test (e.g., choose the forms of null and alternative hypotheses) involving samples from two populations
- Use appropriate statistical techniques to analyze and interpret applications based on data from at least two of the following disciplines: business, economics, political science, administration of justice, life science, physical science, health science, information technology, and education
- Apply simple regression analysis for estimation, inference, and interpret the associated statistics
- Calculate and interpret measures of effect size for many different types of statistical tests, including but not limited to single-sample t-test, independent samples t-test, repeated measures t-test, one-way ANOVA, pearson's r, and chi-square

## Course Content

- Introduction to statistics
- Explain the definitions of statistics, science, and observation
- Populations and samples
- Sampling methods
- Advantages and disadvantages of sampling methods, including but not limited to random sampling, cluster sampling, stratified sampling, and convenience sampling
- The scientific method and the design of research studies
- Variables and measurement
- Scales of measurement as they relate to variables associated with behavioral and social sciences, such as psychology, sociology, economics
- Statistical notation
- Overview of how statistical operations are applied to research within business, social sciences, psychology, life science, health science, and education

- Frequency distributions
- Frequency distribution tables
- Frequency distribution graphs
- The shape of frequency distributions
- Sample spaces

- Central tendency
- The mean
- The median
- The mode
- Selecting a measure of central tendency
- Measures of relative position
- Binomial distributions
- Random variables
- Discrete distributions
- Explanation for how to interpret means and "mean differences" that are reported in behavioral science literature
- Central tendency and the shape of the distribution
- Interpretations of behavioral aspects of data sets with different shapes and skews
- How to report measures of central tendency in the literature using APA format
- Applications of measures of central tendency using data sets from business, social sciences, psychology, life science, health science, and education

- Variability
- The range and the interquartile range
- Standard deviation and variance for a population
- Standard deviations and variance for samples
- How to determine if a sample is biased or unbiased using variance
- How to interpret the behavioral aspects of data sets with relatively small, medium, and large standard deviations
- How to interpret the behavioral aspects of sample data sets by analyzing the relationship between the mean and standard deviation
- How to report the standard deviation in the literature using APA format
- Applications of measures of variability using data sets from business, social sciences, psychology, life science, health science, and education

- Correlation and regression
- The pearson correlation
- Using and interpreting the pearson correlation
- Uses and applications of the pearson correlation to research in the behavioral sciences, including study design, inter-rater reliability, and concurrent validity and construct validity
- Hypothesis tests and the pearson correlation
- Reporting correlations in the literature using APA format
- The point-biserial correlation and measuring effect size with r-squared
- The spearman correlation
- Applications of pearson's r correlation and the spearman correlation using data sets from business, social sciences, psychology, life science, health science, and education
- Introduction to regression
- Analysis and interpretation of data using statistical software, such as SPSS, Excel, or R
- Applications of regression using data sets from business, social sciences, psychology, life science, health science, and education

- Z-scores: location of scores and standardized distributions
- Definition and introduction to z-scores
- Z-scores and location in a distribution
- Using z-scores to standardize a distribution
- Other standardized distributions based on z-scores
- The connection between z-scores and inferential statistics
- Explanation of how inferential statistics are used in the behavioral sciences, such as business, psychology, life sciences, sociology, economics, and education

- Probability
- Explanation of the concept of probability using data sets that are exemplar to behavioral sciences
- Probability and the normal distribution
- Probabilities and proportions for scores in a normal distribution
- The connection between probability and inferential statistics
- Binomial and discrete distributions
- Random variables and expected value

- Probability and samples: the distribution of sample means
- Samples and sampling error
- The distribution of sample means
- Probability and the distribution of sample means
- The standard error of the mean
- How to report the standard error in the literature using APA format
- The connection between the distribution of sample means and inferential statistics
- Binomial distributions

- Introduction to hypothesis testing
- The logic of hypothesis testing within the behavioral sciences
- Uncertainty and errors in hypothesis testing
- Null and alternative hypothesis
- P-value and criteria for a decision of significance
- Z-test as an introduction to hypothesis testing
- Type I and Type II errors
- Single-sample z-test
- Comparison of how one-tailed and two-tailed tests are used within social and behavioral science research, including in disciplines such as business, psychology, sociology, economics, and education
- Measuring effect size with Cohen's d
- Interpretations of Cohen's d
- Importance of effect size and reporting effect size in the literature using APA format
- Applications using data from at least two of the following disciplines: business, economics, political science, administration of justice, life science, physical science, health science, information technology, and education

- Single sample t-test
- Hypothesis tests with a single-sample t-statistic (a t-test with one population)
- Applications of the single-samples t-statistics to research design in the behavioral sciences
- Measuring effect size for the single-sample t-statistic (Cohen's d and r-squared)
- Reporting the results of a t-test in the literature using APA format
- Analysis and interpretation of data using hand calculations and statistical software, such as SPSS, Excel, or R
- Applications using data from at least two of the following disciplines: business, economics, political science, administration of justice, life science, physical science, health science, information technology, and education
- Technology based statistical analysis, such as SPSS, Excel, or R

- The independent samples t-test (between subjects t-test)
- Research design within the behavioral sciences for an independent-samples t-test (a t-test with two populations)
- Hypothesis tests and effect size with the independent-measures t-statistic
- Reporting the results of an independent-measures t-test in the literature using APA format
- Analysis and interpretation of data using hand calculations and statistical software, such as SPSS, Excel, or R
- Applications using data from at least two of the following disciplines: business, economics, political science, administration of justice, life science, physical science, health science, information technology, and education
- Technology based statistical analysis, such as SPSS, Excel, or R

- The repeated measures t-test (within subjects t-test)
- Research design within the behavioral sciences for a repeated measures t-test (within subjects t-test), which is a t-test with two populations
- Hypothesis tests and effect size for the repeated-measures t-test
- Reporting the results of a repeated-measures t-test in the literature using APA format
- Analysis and interpretation of data using hand calculations and statistical software, such as SPSS, Excel, or R
- Technology based statistical analysis, such as SPSS, Excel, or R

- Estimation and confidence intervals
- Overview of estimation
- How to calculate and utilize confidence intervals
- Interpretation of confidence intervals using data sets that are exemplar to behavioral sciences
- Technology based statistical analysis, such as SPSS, Excel, or R

- Introduction to ANOVA
- The logic of analysis of variance
- ANOVA notation and formulas
- Applications of one-way ANOVA to behavioral science research
- The distribution of F-ratios
- Examples of hypothesis testing and effect size with ANOVA
- Post-hoc tests
- Reporting the results of analysis of variance in the literature
- Analysis and interpretation of data using hand calculations and statistical software using APA format
- Technology based statistical analysis, such as SPSS, Excel, or R

- Two-factor analysis of variance
- Introduction to two-factor analysis of variance and how it is used within the behavioral sciences
- Introduction to main effects and interactions
- Interpretations of main effects and interactions
- Technology based statistical analysis, such as SPSS, Excel, or R

- Chi-square test
- Introduction to the non-parametric statistics
- Calculating the chi-square test by hand and also using statistical software, such as SPSS, Excel, or R
- Calculating a chi-square test with no preference
- Calculating a chi-square test from a known population
- Single factor and multiple factor (e.g., 2x2) chi-square tests
- Interpreting the results of chi-square tests
- Effect size measures of chi-square
- Assumptions and limitations of chi-square
- Reporting of chi-square tests using APA-style format
- Technology based statistical analysis, such as SPSS, Excel, or R

## Lab Content

Not applicable.

## Special Facilities and/or Equipment

## Method(s) of Evaluation

Multiple choice quizzes

Hand computations

Weekly homework assignments

Data entry and analysis and interpretation of results using SPSS

Research papers

Summaries and analysis of primary source research articles

Problem-solving exercises

Midterm exams

Final exams

## Method(s) of Instruction

Lectures

In-class group and individual activities

Class discussion

Active learning exercises

## Representative Text(s) and Other Materials

Gravetter, Frederick Jr., and Larry B. Wallnau. __Essentials of Statistics for the Behavioral Sciences, 9th ed.__. 2018.

Sarty, Gordon E.. __Introduction to Applied Statistics for Psychology Students__. 2020.

Sarty text available as OER: https://openpress.usask.ca/introtoappliedstatsforpsych/

## Types and/or Examples of Required Reading, Writing, and Outside of Class Assignments

- Reading assignments:
- Reading and studying of textbook
- Reading and critically analyzing primary source research articles

- Writing assignments:
- Weekly homework assignments
- Interpretations to data analysis
- Computing statistical operations on data