1. Graphic Note Taking By: Marigold Holmes and Kayleen Salchenberg

2. World views influence our research/assessment topic = Epistemologies Qualitative = Meaning & Understanding Chapter 2 – Literature Review Written summary of journal articles, books, past and current state of info. Chapter 2 – Literature Review Written summary of journal articles, books, past and current state of info. Defining Research -What is Research? -What is Assessment? -Why are Research/Assessment important? -Similarities/Differences of Research / Assessment? Defining Research -What is Research? -What is Assessment? -Why are Research/Assessment important? -Similarities/Differences of Research / Assessment? Chapter 1 – Introduction 1.Research Topic (General area of study) 2.Research Problem (Particular aspect of scholarly inquiry) 3.Research Question (Succinct statement or two; what do you want to know?) Chapter 1 – Introduction 1.Research Topic (General area of study) 2.Research Problem (Particular aspect of scholarly inquiry) 3.Research Question (Succinct statement or two; what do you want to know?) Chapter 3 – Methodological Process Describes general way research/ assessment question will be approached. Methodological process is the way in which you are going to be conducting your research. Chapter 3 – Methodological Process Describes general way research/ assessment question will be approached. Methodological process is the way in which you are going to be conducting your research. Steps to Research Steps to Research Steps to Research 1 st step in defining YOUR research/assessment = find your topic so it can guide the process Often lit. reviews help determine the research question Lit. Review informs research topic Observations *Simple = simply observing *Contrived = create a specific scenario Observations *Simple = simply observing *Contrived = create a specific scenario *Write Descriptively *Write w/ discipline *Be validated *Make familiar NEW! How is data collected? Document Analysis Is the research credible (congruent with reality)? Threats to trustworthiness: Credibility= unreliable Transferability= short & specific Dependability= bias unstable Qualitative is good if it is trustworthy Qualitative is good if it is trustworthy How to determine participants Interviews *Structured = universal questions * Semi structured = structured + free flow *Entirely Emergent = completely free-flow Interviews *Structured = universal questions * Semi structured = structured + free flow *Entirely Emergent = completely free-flow Sampling Typical Extreme Homogeneous Maximum Variation Criterion Snowball Sampling Typical Extreme Homogeneous Maximum Variation Criterion Snowball Types of Design Grounded Theory - creation of theory Action - gather research to improve problem Case Study - specific and bound (time, place, etc.) Ethnography - becomes part of community, Narrative - based on spoken/written word-retell Phenomenology -experience w/in a phenomenon Types of Design Grounded Theory - creation of theory Action - gather research to improve problem Case Study - specific and bound (time, place, etc.) Ethnography - becomes part of community, Narrative - based on spoken/written word-retell Phenomenology -experience w/in a phenomenon Focus Groups Organized group discussion with interaction among key members *Structured *Semi-Structured *Entirely Emergent When to use interviews Cannot observe a particular behavior or phenomenon A more controlled inquiry is desired Rich in depth data needed When to use interviews Cannot observe a particular behavior or phenomenon A more controlled inquiry is desired Rich in depth data needed When to use focus groups Multiple perspectives = more or robust data Group interaction increases trustworthiness Data collection time is limited Successful Observations

3. Positivists (THE truth), Interpretive (many interpretations=truth), Postmodernism (individual truth), Critical Social Science (what we know is marred) Variables characteristics that are measured Independent - impacts dependent variable Dependent - depends on action of independent variable Types of Design Experimental - cause and effect using random sampling Quasi Experimental - cause and effect but can’t randomize sample Non-Experimental - surveys and correlations Survey Strength can be locally designed & made to meet specific needs easy to study large populations can be updated, replicated can be used as-is, with permission from the author Limitations Non-response bias – those who don’t respond may have a different opinion, those who do may be similar Surveys that are not good asking people to remember the past double barreled questions – asking two questions at once leading the respondents to a particular answer inaccessible language (ie. use of jargon, slang, or highly technical terms) answers which are not mutually exclusive answers which are not exhaustive Other Considerations Survey organizations Introduction / transitions Time Location Quantitative = Statistical Relationship, causation, correlation Sampling Simple Stratified Systematic Clustering Convenience Snowball How is Data Collected? What is measured How to determine participants How is Data Collected? Non-Experimental Designing the survey Validity relies on reliability Is research valid? Is it reliable? Threats to Validity: (Internal)=*history *maturation *selection *mortality (Treatment)= *diffusion *compensatory equalization/rivalry *demoralization (Procedures)= *testing familiarity *demoralization Extant existing data, information & observations previously collected Strength don’t have to do data collection expanding, building, repurposing Limitations risk of data being outdated population may not fit your target (ie. only subset) since you don’t know the purpose of the data collection previously conducted, there may be external forces you don’t know about don’t know the rigor of the data collection that was conducted Week ７ Notes Ethical Considerations Key Events 1.Nuremberg Code (1940)- Post World War II, running unethical experiments on humans, consent. Result was consent from people to have experiment performed. 2.Thaimaldohide- (1950s-1962)- Medicine/drug in Europe= deformities is babies. Informed consent violated/ FDA needed to regulate 3. Tuskegee Syphilis Study (1932-1972)- Violated African American participants by withholding cure of syphilis in order to understand progression 4.Declaration of Helsinki (1964)- With mounting pressure, this was like a “blown up Nuremberg Code”, research needs to be based on lab/animal experimentation 5.National Research Act & Belmont Report (1974)- Response to Tuskegee, protection of human subjects 6. Common Rule (1981- 1991)- Put into law and is where we are now! Belmont Report: Respect for persons: autonomous agents, diminished autonomy entitled to protection Beneficence- Human subjects should not be harmed, research benefits maximized Justice- benefits of research must be distributed fairly Common Rule: Institutional compliance required, informed consent, requirement for IRB membership, record keeping, stipulate additional protectuoibs for certain vulnerable research subjects

4. Chapter 4 – Analyzing & Interpreting Data Making meaning of the data that is collected Chapter 4 – Analyzing & Interpreting Data Making meaning of the data that is collected Chapter 5 – Discussions Summary of key finds, explanation of results, suggest limitations in the research and make recommendations for future inquiries. Chapter 5 – Discussions Summary of key finds, explanation of results, suggest limitations in the research and make recommendations for future inquiries. Steps to Research Data Preparation 1.Organize data 2.Transcribe data from audio recording/field notes to data *by hand or computer using qualitative computer programs. Data Preparation 1.Organize data 2.Transcribe data from audio recording/field notes to data *by hand or computer using qualitative computer programs. Data Analysis - Coding 1.Get a general sense of data (preliminary exploratory) 2. Code the data (make sense out of text data using labels of segments with codes) 3. Pick one document 4. code the document with brackets and text segment 5. Make a list of all code words 6. Take list and go back to data 7. Reduce lists finding themes and categories The 3 C’s of Analysis Codes = “storage bins” / general headings Categories = consolidation of codes Concepts/Themes = categories as meaningful statements (5-7 themes) * Be sure to check work against original text Effective Narrative Data Analysis Revisit research question frequently Develop a system, test it, then stick with it Review all data again & again Allow concepts to emerge organically Synthesize information without loosing sight of main focus Honor participant voices Use technology as appropriate Data Analysis - Coding 1.Get a general sense of data (preliminary exploratory) 2. Code the data (make sense out of text data using labels of segments with codes) 3. Pick one document 4. code the document with brackets and text segment 5. Make a list of all code words 6. Take list and go back to data 7. Reduce lists finding themes and categories The 3 C’s of Analysis Codes = “storage bins” / general headings Categories = consolidation of codes Concepts/Themes = categories as meaningful statements (5-7 themes) * Be sure to check work against original text Effective Narrative Data Analysis Revisit research question frequently Develop a system, test it, then stick with it Review all data again & again Allow concepts to emerge organically Synthesize information without loosing sight of main focus Honor participant voices Use technology as appropriate Results 1.Summarize findings 2.Convey personal reflections 3. Compare to literature 4. Limitations and suggestions Results 1.Summarize findings 2.Convey personal reflections 3. Compare to literature 4. Limitations and suggestions Use Computer Software for Text identification & retrieval Text analysis Nud*ist NVivo Atlas.ti Ethnograph

5. Data Preparation Score the Data Determine Types of Scores to Analyze Select Statistical Program Input Data Clean & Account for Missing Data Data Preparation Score the Data Determine Types of Scores to Analyze Select Statistical Program Input Data Clean & Account for Missing Data Data Analysis Descriptive statistics describe response to questions an determine overall trends and distribution of data. Inferential statistics – draw conclusions, inferences, or generalizations from a sample to a population of participants. Data Analysis Descriptive statistics describe response to questions an determine overall trends and distribution of data. Inferential statistics – draw conclusions, inferences, or generalizations from a sample to a population of participants. Results Tables, Figures, Presentations – words Results Tables, Figures, Presentations – words We still don’t quite understand