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| A model used to explain what we observe; a simplified explanation |
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| A clear, concise, testable statement; used to provide support for theories |
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| Forms of Quantitative Research |
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Descriptive
Comparative
Correlational
Experimental |
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| describes and numbers the frequency of something (example: sports stats, demographic information) |
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| describes and numbers the frequency of something (example: sports stats, demographic information) |
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| compares two groups on one variable (example: men and women and their love of chocolate) |
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| One group, two variables on a scatterplot (ex: what’s the relationship between height and weight in women?) |
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| A very specific strategy for providing strong evidence for a cause and effect relationship where we manipulate one variable to see its effect on the other, while keeping other variables constant two variables |
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| Numbers that describe data (described using measurements as mean, median, mode, standard deviation, range) |
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| Used to estimate the magnitude of error when measuring samples of a population (example: I'm 95% confident that the average height of women at Vanderbilt is 5'4) |
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| Defines some term using other concepts (example: motivation is the person’s willingness and eagerness to complete a task) |
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| Describes ways for a variable to be measured (example: motivation is defined by class attendance, grade, and preparedness) |
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| Consistency of measurement (example: SAT is a reliable measure of a persons’ aptitude) |
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| How meaningful, useful, and appropriate our conclusions are from tests (SAT is useful for college admissions offices) |
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| The extent to which we can be confident that it was the manipulation of the independent variable that caused the change in the dependent variable, and not some other variable that wasn't controlled for |
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| Our ability to generalize the results of one study to other subjects, settings, and times (can the results of a study about Japanese schools and discipline be applied to American students – probably not, too culturally different) |
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Nominal and Ordinal Data
Depicted using bar graphs or pie charts |
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Interval and Ratio Scale
Depicted using line graphs and histograms |
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Represents difference in the raw score of an individual subject to the mean score of the group to which the individual belongs in units of standard deviation
Z-Score = (Raw score - Group score)/Standard Deviation of Group |
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| Represents difference between mean of the experimental group and mean of the control group in units of standard deviation of the control group |
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| Correlational Coefficient |
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| A number that measures the strength of the relationship between two variables; the power that one variable has in predicting the value of some other variable |
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| Words of Estimative Probability |
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| Terms used to represent the likelihood of a future event occurring; express the extent of a reporter’s confidence in the finding(s) |
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| Terms like "might, may, could, a chance" that are intended to absolve reporter of responsibility to express some level of confidence in the findings |
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| What are the ways of knowing? |
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Definition
Personal Experience
Common Sense
Tradition
Authority
Systematic Inquiry (most reliable if the research design is good and conducted correctly) |
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| What's the risk in believing something is true simply because it's obvious? |
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| People will tend to believe that any reasonable statement about human behavior is obvious, but that does not necessarily make it true. N.L. Gage draws attention to the Baratz Study, where students were assigned to 4 conditions (true statement, opposite, with explanation, w/o explanation) and they all believe that their statement was obvious |
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| What is required before you can say that you know something? |
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Darwin Hunt: 3 Criteria
- it must be correct/true
- it must be justified
- you must be certain of it |
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| What are similarities between qualitative and quantitative research? |
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Definition
- both are forms of systematic inquiry
- both use participants and subjects
- both are subject to IRB approval
- both have tentative results
- both have agreed upon ways of collecting and reporting data |
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| What are differences between qualitative and quantitative research? |
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Definition
Sampling
Role of Researcher
-quant.: detached/unbiased
-qual.:involved/invested
Design
-quant.: predetermined
-qual.: evolving
Data
-quant.: numbers
-qual: words
Goals
-quant.:
-qual.: |
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| 5 rules for making a good graph and be able to fix a wrong one |
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Definition
- Use the correct graph for the data
Categorical: bar graph or pie chart
Continuous: line graph or histogram
- No shading, 3D, or crazy colors
- Be consistent in presentation
- The simpler, the better
- Label graphs, axises, and units |
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| What are five strategies you can use to increase the reliability of your observations? |
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Definition
o Target specific behaviors
o Use low inference measures
o Use multiple judges and train them
o Have them blind to the conditions
o Strive for inter-rater reliability (judges all score the same without collaborating on their scoring decisions) |
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o Autonomy
• Respect for persons (a strategy would be an informed consent form)
• Protect women and children (and unborn babies)
o Beneficence
• First, do no harm (a strategy is to do competent research)
• Second, weight cost/benefit ratio
o Justice
• The benefits and burdens of research are equally distributed |
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| What are the goals and strategies for sampling in qualitative and quantitative research? |
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Definition
o Goals for Qualitative
• Most knowledge and informed participant
o Strategies
• Looks for maximum variation
o Goals for Quantitative
• Most representative sample of the population we want to generalize the results to
o Strategies
• Select random samples
• Stratified sampling (example: proportional representative sample of minor students at Vanderbilt)
• Cluster sampling |
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| How are sampling error, sampling fluctuation, and statistical significance related? How can you reduce sampling error? |
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Definition
o Sampling error is inevitable: difference between sample and population
o Sampling fluctuation is the idea that you’re always going to get a slightly different value from each sample
o Statistical significance is when the difference is larger than you would expect from sampling fluctuation alone
o Reduce sampling error: increase sample size
o Eradicate sampling error: census
o Increase likelihood of statistical significance: increase sample size |
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| What are the required elements of qualitative and quantitative research? |
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Definition
Qualitative
- what is the central phenomenon?
- who are the participants?
- what is the setting?
Quantitative
- what are the subjects
- what are the two variables
- what is the relationship between the variables? |
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| Numbers that describe data (examples: mean, median, mode, standard deviation) |
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| Used to estimate the magnitude of error when measuring samples of a population |
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| Seven Survival Skills according to Tony Wagner |
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Definition
Critical thinking and problem solving skills
Collaboration across networks & Leading by Example
Agility & Adaptability
Initiative & Entrepreneurialism
Effective Oral & Written Communication Skills
Access & Analyzing Data
Curiosity & Imagination |
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| What are 3 Key elements that influence motivation? |
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| What are the 3 Criteria we use when choosing wine? |
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