767 Flashcards
Learn to be a scientist!
How does science differ from a layperson’s approach to science?
- We are explicit in what we’re doing and why we’re doing it. 2. We make a fundamental distinction between observations and inferences. 3. REPLICATION
What’s the difference between observations and inferences?
Observation is the data collected. Inferences are anything else, including results (e.g. tentative conclusions, abstractions about reality, putting words to concepts)
How does the scientific model apply to clinical work?
- Think scientifically 2. Check inferences 3. Identify and be explicit about assumptions we make 4. The goal is to be value-explicit, not value-free
Why should I become aware and explicit of my worldview and assumptions?
The alternative of being explicit and conscious is the risk of imposing values on others and making inaccurate assumptions.
Why do we live in an inductive world in Psychology?
There are no steadfast rules. Must infer from the data we collect.
What is the central problem of inductive science?
Error. All samples have error. Our efforts are all aimed at reducing this error.
What is the aim of science?
Theory.
What is the relevance of the stocastic model to clinical work?
Clients present a sample of their behavior from the population of their experiential world. We must recognize that this is limited, not impose our worldviews, and attempt to understand from multiple vantage points (different theories)
What are Mike Ellis’ current 8 steps of scientific research methods?
- Identify/observe phenomenon 2. Formulate problem. 3. Explicate theorizing. 4. Research hypothesis. 5. Empirical test. 6. Data analysis.7. Interpretation. 8. Revise theory and retest.
What is the impact of paradigms in scientific inquiry?
They affect how we: 1. Construct theory 2. Explain what happens 3. Choose phenomenon to observe and ignore 4. Identify patterns 5. Identify what counts as data
What are the rules of categorization (partitioning)?
- Categories must be set up according to the research problem. 2. Exhaustive 3. Mutually exclusive and independent 4. Each category (variable) is derived from one classification principle 5. Any categorization scheme must be on one level of discourse
Why should I graph my data?
Shows relations and their nature. Can show things you would miss with simple statistics (e.g. bimodal data)
What might cause negative results?
- Incorrect theory and hypotheses 2. Inappropriate or incorrect methodology 3. Inadequate or poor measurement 4. Faulty analysis
What is the core procedure of statistical analysis?
- Set up chance expectation as hypothesis (null hypothesis) 2. Try to fit empirical data to chance model 3. If empirical data fit chance model, they are not statistically significant. If data do not fit chance model, they are statistically significant
What is the law of large numbers?
With an increase in the size of sample, n, there is a decrease in the probability that the observed value of an event, A, will deviate from the true value of A by no more than a fixed amount, k. (Reduction of errors)
What are characteristics of the normal curve?
- Unimodal 2. Symmetrical 3. Possesses certain mathematical properties
What is Popper’s major contribution to scientific inquiry?
Falsificationism!
What is the key principle of falsificationism?
Aim to disprove, not prove, theory.
What are Jon Stewart Mill’s five methods of elimination?
- Method of agreement (constant conjunction) 2. Method of differences (absence of cause or absence of effect) 3. Method of joint agreement of differences (basis for control group vs. experimental group) 4. Method of residuals (notion of error) 5. Method of concomitant variance *look at how variables vary together if can’t manipulate– groundwork for correlation)
What is Jon Stewart Mill known for in scientific method?
- Eliminating rival explanations 2. Testing multiple hypotheses 3. Control groups 4. Manipulating variables
What are Hume’s principles for inferring causality?
- Contiguity (events occur close together in space and time) 2. Temporal preference (A must come before B if it is cause of B) 3. Constant conjunction (A must always be present when B is present if A causes B
What is a better term for post-positivism?
Sophisticated Falsificationism
What are principles of post-positivism?
- Test with multiple competing hypotheses 2. Theory is viable to the extent to which it has been systematically been subject to and survived multiple attempts to confirm and disconfirm it 3. Disconfirmation is more effective
Two ways of defining a set.
- List - listing all members of a set. 2. Rule - giving a rule for determining whether objects belong to a set
Two basic set operations
- Intersection – overlap of two or more sets (A AND B) 2. Union - Set that contains all members of one set and all members of another set (A OR B)
What are the 7 steps of the General Linear Model (GLM)
- Specify the statistical hypotheses 2. Specify the statistical model 3. Estimate parameters of the model 4. Assess goodness of fit/effect size 5. Test hypotheses on the model parameters 6. Assess the adequacy of the model 7. Make a decision (to accept or reject the statistical hypotheses) based on this… accept or reject the research hypotheses
What are the four scales/levels of measurement?
- Nominal 2. Ordinal 3. Interval 4. Ratio
How do you graph nominal data?
Bar, pie, or histogram (histogram if variable is on a continuum)
How do you graph ordinal data?
Frequency distribution, bar, pie, or histogram
How do you graph interval data?
Line graphs, percentiles, histograms, frequency distributions, bar, pie, histogram
How do you graph ratio data?
Cartesian plots, anything you can do with lower levels of measurement
Central tendency
Mode, median, mean
Spread
Range, inclusive range (high - low +1), variance, standard deviation
What measures of central tendency are appropriate for nominal data?
Mode only
What measures of central tendency are appropriate for ordinal data?
Mode and median
What measures of central tendency are appropriate for interval data?
Mode, median, and mean
What measures of central tendency are appropriate for ratio data?
Mode, median, and mean (and some things we don’t use)
What measures of variability are appropriate for nominal data?
None
What measures of variability are appropriate for ordinal data?
Range only
What measures of variability are appropriate for interval data?
Range, variance, and SD
What measures of variability are appropriate for ratio data?
Range, SD, SD squared
Properties of Z scores
Mean = 0 V = 1 SD = 1
How are sums of squares inflated?
Dependent on sample size. Dependent on dispersion. Bigger sample size, bigger sums of squares
Types of probability
A priori and a posteiori
Why do we learn statistics?
- We live in an inductive world and all samples have error. 2. Statistics help us assign probability to effects being produced by chance alone.