Exam 2 Flashcards
Census
every person in population
Confidence interval
Range in which a population value is likely to fall
Focus group
Unstructured group interview
Frequency curve
Frequencies with line instead of bar
Grouped frequency distribution
Combined adjacent values into categories to measure frequencies of those categories
Histogram
bars touching each other and indicates that the variable is quantitative
Margin of error
Indicating true value of population will fall between the listed values
Mean deviation
Score on variable minus mean of variable
Oversampling
Larger proportion of strata than actually represented in the population
Sampling bias
When one of the two are not met: entire population could be sampled from, all selected individuals are actually sampled
Standard deviation
Square root of variance
Stem and leaf plot
Graphically summarizes raw data and can see the data values
Systematic random sampling
Take every X person on the list
Variance
Sum of squares divided by sample size (N)
Beta
Probability of a scientist making a type 2 error
Binomial distribution
Sampling distribution for events that have two equally likely possibilities
Effect size
Indicates the magnitude of the relationship that is not influenced by sample size
Power
The probability the researcher will accurately reject the null hypothesis (1-beta)
P (probability) value
likelihood of an observed statistic occurring on the basis of the sampling distribution
Proportion of explained variabililiy
Proportion of dependent variable that is “explained by” the independent variable
Type 1 Error
False negative
Type 2 Error
False positive
Beta weights
Same as regression coefficient
Chi square statistic
Relationship between two nominal variables
Common-causal variable
Variables that is not part of research hypothesis but causes both predictor and outcome variable
Contingency table
Number of individuals in each of the combinations of the two nominal variables
Cross-sectional research design
Research designs that measure people from different age groups at the same time
Extraneous variables
Variables other than predictor variable that cause outcome variable, but do not cause predictor variable
Independent relationship
Distribution of points is random
Mediating variable (mediator)
Variable caused by predictor variable that in turn causes outcome variable
Multiple correlation coefficient (R)
Ability of all predictor variables together to predict outcome variable (statistical significance tested with F)
Multiple regression
Pearson correlation coefficients both between each of the predictor variables and the outcome variable
Reciprocal causation
The two variables case each other
Regression coefficients
Relationship between each of the predictor variables and outcome variable
Restriction of range
Most participants have similar scores on one of the variables being correlated so the study does not cover the full range of variables
Spurious relationship
Predictor and outcome variables both caused by same variable
Analysis of variance (ANOVA)
Compare the mean of a dependent variable across levels of an experimental research design (nominal independent)
Degrees of freedom
between groups design = # levels of IV -1
within groups design = # participants - # of conditions
F
between groups variance/within-groups variance
t-test
Used to compare two group means using either a between participants design (independent samples t-test) or repeated measures design (paired-samples t-test)
Advantage of within-groups design
Grater statistical power
Artifacts
Aspects of the research methodology that may go unnoticed and may inadvertently produce confounding variables
Demand characteristics
Aspects of research that allow participants to guess hypothesis
Internal analysis
Computing a correlation of scores on the manipulation check measure with the scores on the dependent variable as an alternative test of research hypothesis
Matched-group research design
Participants dependent variable initially measured, assigned to conditions and then measured again
