20809230Statistics for the Social Sciences
Course Information
Description
Students will develop statistical knowledge and skills through problem solving in the social sciences. Course components focus on measuring variables, measures of central tendencies, the utility of descriptive statistics, and introduction to inferential statistics and its predictive nature, the differences between samples and populations, and the increased capacity to read and display statistical information. Work is completed by hand and through statistical software.
Total Credits
4
Course Competencies
  1. Analyze the importance of statistics in everyday life
    Assessment Strategies
    In-class assignment
    Criteria
    Evaluate use of statistics in everyday life
    Analyze the importance of statistics in the social sciences and the furthering of scientific knowledge

  2. Employ appropriate language in describing research data
    Assessment Strategies
    In-class assignment and semester project
    Criteria
    Investigate levels of measurement and levels and association
    Differentiate between population and sample data
    Differentiate between qualitative and quantitative data
    Explain theories of sampling
    Compare descriptive statistics and inferential statistics
    Identify different methods for collecting data and their appropriate use

  3. Analyze data using calculator and appropriate software when available
    Assessment Strategies
    Homework and semester project
    Criteria
    Calculate solutions to exercises
    Distinguish between variables and cases
    Define new variables with appropriate labels and codes
    Label new variables according to level of measurement
    Code data and enter data according to variable definitions
    Save data file and send data file electronically

  4. Employ appropriate representations of sample data
    Assessment Strategies
    In-class assignment and/or homework
    Criteria
    Generate a frequency list, by hand, showing both relative frequencies and cumulative frequencies from a given data set
    Use software to generate a frequency list showing both relative frequencies and cumulative frequencies from a given data set
    Construct a table, bar chart, histogram, and scatter plot using frequency list
    Determine appropriate representation of data given type of data and the use of representation

  5. Calculate measures of central tendency
    Assessment Strategies
    In-class assignment, homework, quiz and/or exam
    Criteria
    Calculate the mean of a series of data points
    Calculate the median of a series of data points
    Calculate the mode of a series of data points
    Demonstrate the successful use of software to calculate central tendencies
    Use mean, median, and mode given a set of data points

  6. Calculate measures of dispersion
    Assessment Strategies
    In-class assignment, homework, quiz and/or exam
    Criteria
    Calculate the range and interquartile range of a series of data points
    Calculate the standard deviation and variance of a series of data points
    Differentiate between population and sample measures of dispersion
    Demonstrate the successful use of software to calculate measures of dispersion
    Demonstrate application of measures of dispersion

  7. Calculate and compare distributions
    Assessment Strategies
    In-class assignment, homework, quiz and/or exam
    Criteria
    Demonstrate the concept of the Normal Curve
    Calculate Z scores
    Interpret Z score distributions appropriately and accurately
    Compare and contrast distributions in respect to central tendencies
    Compare and contrast distributions in respect to dispersions
    Interpret differences of distributions in practical problems in the social sciences

  8. Recode data and create composite variables
    Assessment Strategies
    In-class assignment and homework
    Criteria
    Recode variables
    Compare and contrast recoded variables with original variables
    Determine when and why to recode variables
    Construct composite variables
    Compare and contrast composite variables with original variables
    Determine when and why to construct composite variables
    Interpret the application of recoded and composite variables in practical problems in the social sciences

  9. Calculate probability, estimation, and differentiate inferential statistics
    Assessment Strategies
    In-class assignment, homework, quiz and/or exam
    Criteria
    Explain the purpose behind inferential statistics in regards to generalizing from the sample to the population
    Demonstrate randomized sampling and sampling techniques
    Define and identify sampling key terms such as population, sampling, parameter, statistic, probability sampling, and standard error of the mean
    Demonstrate two theorems involved in the sampling process
    Define sampling distribution and its characteristics
    Show competence in communicating various statistical symbols for distribution characteristics, such as sample mean, population mean, sample standard deviation, and population standard deviation
    Demonstrate understanding of estimation
    Calculate confidence intervals for sample means and for sample proportions
    Interpret the application of confidence intervals and estimation in social science problems

  10. Test hypotheses
    Assessment Strategies
    In-class assignment, homework, quiz, and/or exam
    Criteria
    Explain the logic of hypothesis testing
    Apply key terms, such as null hypothesis, alpha level and test statistic
    Describe how and why one would “reject the null hypothesis”
    Describe the differences between one-tailed and two-tailed tests and when to apply one over the other
    Differentiate between Type I and Type II errors
    Test, through calculation, significance of one sample case in terms of means and proportions in both small and large sample sizes
    Test, through calculation, significance of two sample case in terms of means and proportions in both small and large sample sizes
    Test, through calculation, hypotheses using analysis of variance and chi square
    Apply key concepts, such as population variance, various sum of square concepts, and the difference between statistical significance and importance
    Interpret the application of ANOVA and Chi Square in social science problems

  11. Determine the factors of bivariate association and measures of association
    Assessment Strategies
    In-class assignment, homework, quiz, and/or exam
    Criteria
    Use measures of association to describe and analyze importance of relationships compared to their statistical significance
    Demonstrate purpose of bivariate tables in terms of defining associations between variables and distributions
    Describe association in terms of existence, strength, and pattern or direction
    Calculate percentages for a bivariate table and interpret results
    Use software to compute measures of association and successfully interpret results.
    Calculate Phi, Cramer’s V, and Lambda by hand and through SPSS
    Identify the proportional reduction in error
    Demonstrate measures of association at nominal, ordinal, and interval-ratio levels of analysis
    Calculate Gamma and Spearman’s Rho
    Interpret a scattergram in terms of a bivariate relationship
    Calculate and interpret slope, Y intercept, and Pearson’s r and Pearson’s r2
    Calculate least-squares regression line and be able to use it to predict values of Y
    Demonstrate total, explained, and unexplained variance in terms of bivariate relationship at interval-ratio level
    Use software to calculate and interpret measures of association at interval-ratio level
    Use software to calculate correlation matrix and successfully interpret results
    Demonstrate bivariate measures of association as applied to social science problems

  12. Correlate Variables in Multiple Regression
    Assessment Strategies
    In-class assignment, homework, quiz and/or exam
    Criteria
    Calculate and interpret partial correlation coefficients
    Calculate the least-squares multiple regression equation with partial slopes and be able to interpret results
    Calculate multiple correlation coefficient and interpret results
    Differentiate between direct, spurious, and intervening relationships within multiple regression models
    Predict Y values within multiple regression model
    Demonstrate multivariate measures of association as applied to social science problems

  13. Construct survey, complete analysis, and report results
    Assessment Strategies
    Survey, data collection with survey instrument, data analysis and written interpretation
    Criteria
    Construct a ten-item survey instrument focusing on topic given in class
    Survey at least twenty respondents
    Code survey instrument
    Define survey variables into software or spreadsheet and enter data from completed surveys
    Complete frequency distributions for variables, bivariate analysis, and construct a composite variable
    Write an interpretation of results, including purpose of survey, proposed hypotheses, questions for future research, and provide at least one graphic display of results