res710 | Graduate

Statistical Research Methods And Design I

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This course introduces students to an array of quantitative research methods and their appropriate application in empirical research. Students will be introduced to basic statistical concepts, theory and the assumptions that govern the methodology. An overview of descriptive and inferential statistics, including nonparametric statistics will be provided. The analysis of data, data visualization and the language used for the presentation of data in the social sciences will be emphasized. Competency A: Analyze peer reviewed quantitative research and explain how the results of quantitative analysis can inform data driven decision making. Competency B: Explain the fundamentals of quantitative data analysis procedures, assumptions, and their appropriateness to different types of research design. Competency C: Discuss philosophical theories and fundamentals of quantitative methods and their applications to different types of research problems. Competency D: Determine appropriate data analysis procedures for a quantitative research design and demonstrate how to use various quantitative data analysis procedures. Competency E: Explain parametric and nonparametric statistical procedures and their appropriate use. Competency F: Conduct quantitative data analysis using statistical analysis software (IBM/SPSS) and interpret results. Competency G: Evaluate the reliability and validity of quantitative data analysis techniques. Competency H: Compose brief reports to present results of statistical data analysis.

This graduate-level course is 8 weeks This course is available to take individually or To enroll, speak with an Enrollment Representative.

Course details:

Credits: 3
Continuing education units: XX
Professional development units: XX
Duration: 8 weeks

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    Visual Examination of Data

    • Competency G: Evaluate the reliability and validity of quantitative data analysis techniques.[Wk 2]
    • Competency H: Compose brief reports to present results of statistical data analysis. [Wk 2]
    • Competency A: Analyze peer reviewed quantitative research and explain how the results of quantitative analysis can inform data driven decision making. [Wk 2]
    • Competency B: Explain the fundamentals of quantitative data analysis procedures, assumptions, and their appropriateness to different types of research design. [Wk 2}
    • Competency C: Discuss philosophical theories and fundamentals of quantitative methods and their applications to different types of research problems. [Wk 2}
    • Competency D: Determine appropriate data analysis procedures for a quantitative research design and demonstrate how to use various quantitative data analysis procedures. [Wk 2]
    • Competency E: Explain parametric and nonparametric statistical procedures and their appropriate use. [Wk 2]
    • Competency F: Conduct quantitative data analysis using statistical analysis software (IBM/SPSS) and interpret results.[Wk 2]

    Dispersion and Central Tendency

    • Competency A: Analyze peer reviewed quantitative research and explain how the results of quantitative analysis can inform data driven decision making. [Wk 3]
    • Competency B: Explain the fundamentals of quantitative data analysis procedures, assumptions, and their appropriateness to different types of research design. [Wk 3}
    • Competency C: Discuss philosophical theories and fundamentals of quantitative methods and their applications to different types of research problems. [Wk 3}
    • Competency D: Determine appropriate data analysis procedures for a quantitative research design and demonstrate how to use various quantitative data analysis procedures. [Wk 3]
    • Competency E: Explain parametric and nonparametric statistical procedures and their appropriate use. [Wk 3]
    • Competency F: Conduct quantitative data analysis using statistical analysis software (IBM/SPSS) and interpret results.[Wk 3]
    • Competency G: Evaluate the reliability and validity of quantitative data analysis techniques.[Wk 3]
    • Competency H: Compose brief reports to present results of statistical data analysis. [Wk 3]

    Normal Distribution Inferential Statistics

    • Competency B: Explain the fundamentals of quantitative data analysis procedures, assumptions, and their appropriateness to different types of research design. [Wk 4}
    • Competency A: Analyze peer reviewed quantitative research and explain how the results of quantitative analysis can inform data driven decision making. [Wk 4]
    • Competency C: Discuss philosophical theories and fundamentals of quantitative methods and their applications to different types of research problems. [Wk 4}
    • Competency D: Determine appropriate data analysis procedures for a quantitative research design and demonstrate how to use various quantitative data analysis procedures. [Wk 4]
    • Competency E: Explain parametric and nonparametric statistical procedures and their appropriate use. [Wk 4]
    • Competency F: Conduct quantitative data analysis using statistical analysis software (IBM/SPSS) and interpret results.[Wk 4]
    • Competency G: Evaluate the reliability and validity of quantitative data analysis techniques.[Wk 4]
    • Competency H: Compose brief reports to present results of statistical data analysis. [Wk 4]

    Testing Difference Between Two or Multiple Means

    • Competency B: Explain the fundamentals of quantitative data analysis procedures, assumptions, and their appropriateness to different types of research design. [Wk 6}
    • Competency C: Discuss philosophical theories and fundamentals of quantitative methods and their applications to different types of research problems. [Wk 6}
    • Competency D: Determine appropriate data analysis procedures for a quantitative research design and demonstrate how to use various quantitative data analysis procedures. [Wk 6]
    • Competency E: Explain parametric and nonparametric statistical procedures and their appropriate use. [Wk 6]
    • Competency F: Conduct quantitative data analysis using statistical analysis software (IBM/SPSS) and interpret results.[Wk 6]
    • Competency G: Evaluate the reliability and validity of quantitative data analysis techniques.[Wk 6]
    • Competency H: Compose brief reports to present results of statistical data analysis. [Wk 6]
    • Competency A: Analyze peer reviewed quantitative research and explain how the results of quantitative analysis can inform data driven decision making. [Wk 6]

    Non Parametric Tests: Analysis of Categorical Data

    • Competency B: Explain the fundamentals of quantitative data analysis procedures, assumptions, and their appropriateness to different types of research design. [Wk 8}
    • Competency C: Discuss philosophical theories and fundamentals of quantitative methods and their applications to different types of research problems. [Wk 8}
    • Competency D: Determine appropriate data analysis procedures for a quantitative research design and demonstrate how to use various quantitative data analysis procedures. [Wk 8]
    • Competency E: Explain parametric and nonparametric statistical procedures and their appropriate use. [Wk 8]
    • Competency F: Conduct quantitative data analysis using statistical analysis software (IBM/SPSS) and interpret results.[Wk 8]
    • Competency G: Evaluate the reliability and validity of quantitative data analysis techniques.[Wk 8]
    • Competency H: Compose brief reports to present results of statistical data analysis. [Wk 8]
    • Competency A: Analyze peer reviewed quantitative research and explain how the results of quantitative analysis can inform data driven decision making. [Wk 8]

    Fundamentals of Quantitative Research

    • Competency A: Analyze peer reviewed quantitative research and explain how the results of quantitative analysis can inform data driven decision making. [Wk 1]
    • Competency B: Explain the fundamentals of quantitative data analysis procedures, assumptions, and their appropriateness to different types of research design. [Wk 1}
    • Competency C: Discuss philosophical theories and fundamentals of quantitative methods and their applications to different types of research problems. [Wk 1}
    • Competency D: Determine appropriate data analysis procedures for a quantitative research design and demonstrate how to use various quantitative data analysis procedures. [Wk 1]
    • Competency E: Explain parametric and nonparametric statistical procedures and their appropriate use. [Wk 1]
    • Competency F: Conduct quantitative data analysis using statistical analysis software (IBM/SPSS) and interpret results.[Wk 1]
    • Competency G: Evaluate the reliability and validity of quantitative data analysis techniques.[Wk 1]
    • Competency H: Compose brief reports to present results of statistical data analysis. [Wk 1]

    Correlation and Regression

    • Competency C: Discuss philosophical theories and fundamentals of quantitative methods and their applications to different types of research problems. [Wk 7}
    • Competency D: Determine appropriate data analysis procedures for a quantitative research design and demonstrate how to use various quantitative data analysis procedures. [Wk 7]
    • Competency E: Explain parametric and nonparametric statistical procedures and their appropriate use. [Wk 7]
    • Competency A: Analyze peer reviewed quantitative research and explain how the results of quantitative analysis can inform data driven decision making. [Wk 7]
    • Competency B: Explain the fundamentals of quantitative data analysis procedures, assumptions, and their appropriateness to different types of research design. [Wk 7}
    • Competency F: Conduct quantitative data analysis using statistical analysis software (IBM/SPSS) and interpret results.[Wk 7]
    • Competency G: Evaluate the reliability and validity of quantitative data analysis techniques.[Wk 7]
    • Competency H: Compose brief reports to present results of statistical data analysis. [Wk 7]

    Testing One Sample Mean

    • Competency A: Analyze peer reviewed quantitative research and explain how the results of quantitative analysis can inform data driven decision making. [Wk 5]
    • Competency B: Explain the fundamentals of quantitative data analysis procedures, assumptions, and their appropriateness to different types of research design. [Wk 5}
    • Competency C: Discuss philosophical theories and fundamentals of quantitative methods and their applications to different types of research problems. [Wk 5}
    • Competency D: Determine appropriate data analysis procedures for a quantitative research design and demonstrate how to use various quantitative data analysis procedures. [Wk 5]
    • Competency E: Explain parametric and nonparametric statistical procedures and their appropriate use. [Wk 5]
    • Competency F: Conduct quantitative data analysis using statistical analysis software (IBM/SPSS) and interpret results.[Wk 5]
    • Competency G: Evaluate the reliability and validity of quantitative data analysis techniques.[Wk 5]
    • Competency H: Compose brief reports to present results of statistical data analysis. [Wk 5]
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