This course introduces students to data analysis in the child and youth care field, including both theoretical and practical perspectives. The first half of the course is spent considering a variety of quantitative techniques. The remainder of the course explores qualitative analysis and its guiding principles.
The following global ideas guide the design and delivery of this course:
- An understanding of how to generate, shape and refine a research problem is important in CYC practice.
- An understanding of the principles of descriptive and inferential statistics is essential to analyze research results.
- Descriptive statistics are useful answering practice related research questions. Researchers can create and interpret frequency distributions; create histograms; complete measures of central tendency and variability.
- Practice-based hunches can be explored by hypothesis testing. As students begin to understand the differences between descriptive and inferential statistics, they will explore concepts of random error/chance, and examine the role of null and research hypotheses in making statistical decisions.
- Effective use of data analysis supports practice-based research.
- The Pearson chi-square test can examine a practice question from two ways at once.
- Independent and paired t-tests can be used to answer practice-based research questions.
- Qualitative and quantitative research is used by practitioners to answer practice related research questions. Human services professionals continually gather qualitative data, assess it and revise it in the face of new information. Effective research stresses the importance of analyzing and synthesizing qualitative data for making informed decisions in practice.
- Understanding of the diversity in theoretical perspectives to qualitative research, a practitioner recognizes the kinds of research questions that can be addressed qualitatively by various theoretical perspectives. Each perspective also informs the clinical interview and strategies for data collection.
- For the qualitative researcher, immersion is the process of living with and listening to the data.
- Coding is used to break down data into more manageable pieces for analysis. Appropriate measures for ensuring the rigor of qualitative work must be in place as the researcher codes qualitative data into meaning units.
- The process of qualitative analysis reduces the amount of data into segments of text which the researcher uses to identify and develop themes. The discussion of themes is the outcome of the research.
Methods of Instruction
- Group Work
- Student presentations
- Audiovisual presentations
Means of Assessment
This course will conform to Douglas College policy regarding the number and weighting of evaluations. Typical means of evaluation would include a combination of:
- written assignments
- case evaluation
- group presentations
This is a Graded Course.
Upon successful completion of this course, the student will be able to:
- generate, shape and refine a research problem
- discuss the differences between descriptive and inferential statistics
- discuss the role of descriptive statistics in answering practice related research questions
- discuss specific research measures
- discuss basic concepts related to hypothesis testing
- explain the differences between parametric and non-parametric inferential statistics
- use chi squared and t-tests (independent and paired) appropriately
- compare the processes of qualitative and quantitative data analysis
- recognize the kinds of research questions that can be addressed qualitatively by different theoretical perspectives
- discuss the influence of the qualitative researcher on the research situation, data collection and analysis
- discuss appropriate measures for ensuring rigor in qualitative research
- code data for qualitative analysis
- identify themes in qualitative analysis.
Course Guidelines for previous years are viewable by selecting the version desired. If you took this course and do not see a listing for the starting semester/year of the course, consider the previous version as the applicable version.
Below shows how this course and its credits transfer within the BC transfer system.
A course is considered university-transferable (UT) if it transfers to at least one of the five research universities in British Columbia: University of British Columbia; University of British Columbia-Okanagan; Simon Fraser University; University of Victoria; and the University of Northern British Columbia.
For more information on transfer visit the BC Transfer Guide and BCCAT websites.
If your course prerequisites indicate that you need an assessment, please see our Assessment page for more information.