Research Design, Statistics, Tests, and Measurements Flashcards
tentative and testable explanation of the relationship between two or more variables
Hypothesis
characteristic or property that varies in amount or kind, and can be measured
Variable
state how the researcher will measure the variables
Operational definitions
Independent variable
variable whose effect is being studied and the variable that the experimenter manipulates
Dependent variable
the response that is expected to vary with differences in the independent variable
Correlational study
IV is not manipulated
Naturalistic observation
researcher does not intervene; measures behavior as it naturally occurs
Quasi-experiment
IV manipulated but subjects not randomly assigned to groups
True experiment
IV manipulated and subjects randomly assigned to groups
the group to which the researcher wishes to generalize their results
Population
subset of the population
Sample
Random selection
each member of the population has an equal chance of being selected for the sample
Stratified random sampling
each subgroup of the population is randomly sampled in proportion to its size
Representative sample
the sample matches as many characteristics as possible of the population as a whole
each subject is exposed to only one level of each independent variable
Between-sample design
match subjects on the basis of the variable that they want to control
Matched-subjects design
using the same subjects in both groups
Within-subjects (repeated-measures) design
all subjects will experience both levels, just in different orders
Counterbalancing
Confounding variables
unintended independent variables that could differently affect the dependent variable
treating both groups equally in all respects except for one variable
Control group design
control group is not necessarily similar to the experimental group since the researcher doesn’t use random assignment
Nonequivalent-group design
due to their expectations, the experimenter might inadvertently treat groups of subjects differently
Experimenter bias
neither the researcher who interacts with the subjects nor the subjects themselves know which groups received the IV or which level of the IV
Double-blinding
Demand characteristics
any cues that suggest to subjects what the researcher expects from them
Hawthorne effect
tendency of people to behave differently if they know that they are being observed
External validity
how generalizable the results are
Descriptive Statistics
Organizing, describing, quantifying, and summarizing a collection of actual observations
Frequency Distributions
Graphic representation of how often each value occurs
value of the most frequent observation in a set of scores
Mode
two values are tied for being the most frequently occurring observation
Bimodal
middle value when observations are ordered from least to greatest, or from greatest to least
Median
arithmetic average
Mean
Outliers
extreme scores; mean is most sensitive
Range
smallest number in the distribution subtracted from the largest number
Standard deviation
typical distance of scores from the mean
Variance
square of the SD and is a description of how much each score varies from the mean
tells us the percentage of scores that fall at or below that particular score
Percentile
indicates the number of standard deviations a score is away from the mean
z-Score
Normal distribution
about 68 percent of scores fall within 1 SD of the mean; about 96 percent of scores fall within 2 SD of the mean
t-Scores
distribution has a mean of 50 and a SD of 10
Correlation Coefficients
Measure to what extent, if any, two variables are related
change in value of one variable tends to be associated with a change in the same direction of the value of the other variable
Positive correlation
a change in value of one variable tends to be associated with a change in the opposite direction of the other variable
Negative correlation
graphical representation of correlational data
Scatterplot
attempts to account for the interrelationships found among various variables by seeing how groups of variables “hang together”
Factor analysis
Inferential Statistics
Use a relatively small batch of actual observations to make conclusions about the entire population of interest
Draw conclusions about population based upon research conducted on samples
Significance Testing
mistakenly reject the null hypothesis
Type I error
accept the null hypothesis when it is, in fact, false
Type II error
Beta
probability of making a Type II error
t-Tests
used to compare the means of two groups
ANOVA
estimate how much group means differ from each other by comparing the between-groups variance to the within-group variance using a ratio (F ratio)
Factorial design
each level of a given IV occurs with each level of the other IV
Interaction
when the effects of one IV are not consistent for all levels of the other IV
Chi-square test
used when individual observations are names or categories
Meta-Analysis
Used to make conclusions on the basis of data from different studies
assessing an individual’s performance in terms of how that individual performs in comparison to others
Norm-referenced testing
concerned with the question of what the test taker knows about a specified content domain
Domain-referenced (criterion-referenced) testing
Consistency with which a test measures whatever it is that the test measures
Reliability
Test-retest method
same test is administered to the same group of people twice
Alternate-form method
examinees are given two different forms of a test that are taken at two different times
Split-half reliability
test takers take only one test that is divided into equal halves
Extent to which a test actually measures what it purports to measure
Validity
test’s coverage of the particular skill or knowledge area that it is supposed to measure
Content validity
whether or not the test items appear to measure what they are supposed to measure
Face validity
how well the test can predict an individual’s performance on an established test of the same skill or knowledge area
Criterion validity
when a test is used to predict future performance
Predictive validity
when a test is given at the same time as the criterion measure
Concurrent validity
testing the criterion validity of a test on a second sample, after you demonstrated validity using an initial sample
Cross validation
how well performance on the test fits into the theoretical framework related to what it is you want the test to measure
Construct validity
performance on the test is not correlated with other variables that the theory predicts that test performance should not be related to
Discriminant validity
Nominal (categorical) scale
labels observations so that observations can be categorized
Ordinal scale
observations are ranked in terms of size or magnitude
Interval scale
actual numbers
Ratio scale
there is a true zero point that indicates the total absence of the quantity being measured
Aptitude tests
used to predict what one can accomplish through training; predict future performance
Achievement tests
attempt to assess what one knows or can do now
Adaptive test
computerized achievement test that adapts to the test taker’s ability by assessing the accuracy of previously answered questions
how far away a person’s score is from the average score for the particular age group the subject is a member of
Deviation IQ
self-rating device usually consisting of somewhere between 100-500 statements where there is a limited number of ways to respond; MMPI
Personality inventory
test taker is presented with stimuli and asked to interpret what they see; Rorschach, TAT
Projective tests
Barnum effect
tendency of people to accept and approve of the interpretation of their personality that you give them
