MIDTERM TEST REVIEW
PSYC 4370
I.Week 1Scientific Method
present day attitudes toward the scientific method
how is research commonly used
five different ways of knowing truth
basic propositions or presuppositions of the scientific method and how it is different from the other four ways of knowing
advantages of the scientific method in research
logical progression in science (describe behavior, predict behavior, determining causes of behavior, explain behavior)
measurement: definition
scales of measurement: nominal (categorical), ordinal (ranking), interval/ratio (numerical)
variables: discrete vs. continuous
concepts: population, sample, statistic, parameter
II.Week 2Frequency, Central Tendency, Variability, Z Scores
frequency: how often something occurs; goal in science is to discover repeatable patterns
creating intervals: how many, how big (too few, too many)
Ways of looking at data: proportions, percentile points, percentile ranks
ways of representing data: graphs (which axis?)
Bar graphs, histograms, polygons
Shapes of curves: symmetrical, skewness (negative, positive)
central tendency: what is it attempting to measure or represent
mode, median, mean: define, advantages & disadvantages of each; when to use each
variability (diversity compared to mean): range, deviation, variance (definitions, what information does each give us)
standard deviation: what is it measuring, what are its strengths
normal curve: why do we study the normal; its assumptions & properties; areas under curves measures proportions or percentages of scores; what percentages for each standard deviation
standard (z) scores: individual raw score only makes sense with reference to the distribution; standard scores transform raw scores into scores defined by the standard deviation (know formula for z score); determines position in distribution by how many how many standard deviations point is with reference to the mean
how do z scores allow for comparisons with other distributions?
Using Z scores to change scales of a distribution with a new mean & new standard deviation
Proportions, percentiles, percentile ranks
do problems using the z score table
- Week 3 Linear Regression, Correlation
descriptive statistics: what is its goal; what does it tell us
one variable: frequency, central tendency, variability, z scores
two or more variables: linear regression, correlation
relationships: what do we mean, what are we looking for
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linear relationships: existence of relationships, types of relationships, directions of relationships, strength of relationships (perfect vs. imperfect)
regression line: purpose, what does it measure, what is its uses, how to calculate
uses raw scores; line has slope & y-intercept; for an imperfect relationship there will always be prediction errors (can minimize errors by calculating the standard error of estimate, similar to standard deviation in properties)
correlation: what is it & what does it measure, its purposes, similarities to regression
expressed as a coefficient calculation: most common is Pearson’s r; range of coefficient scores, Pearson’s r converts raw scores to z scores (correlation expressed in terms of z scores) & measures magnitude and direction of relationship
compare graph hand-out to compare regression & correlation (regression has slope & y-intercept using raw scores; Pearson’s r is the slope of the regression line transformed into z scores with y-intercept going through zero)
Other correlation coefficients (not on test):
Spearman’s: used for ordinal data
Eta: used for curvilinear relationships
correlation & causation; possible explanations for correlation; experimental method is only way to show causation
Be able to do regression & correlation calculations & interpret or apply results
Midterm Exam is worth 70 points.
You will need Scantron Sheet #882.
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