is: 1. presumed cause, antecedent, predictor variable, treatment(s), factor(s), denoted by X. 2.
also, a manipulated variable in an experiment or study whose presence or degree determines the change in the dependent variable. 3. independent variables may have one or more levels.
Dependent variable
is: 1.
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outcome measure, presumed effect, conquence, variable predicted to, criterion, observation, denoted by Y. 2. also, the observed variable in an experiment or study whose changes are determined by the presence or degree of one or more independent variables.
True experiment VS.
1. investigator uses random assignment of subjects to treatment groups. 2. used to establish a cause and effect relationship.
1. investigator must deal with intact groups. 2. used to establish a cause and effect relationship. 3. investigator manipulates independent variables.
Active independent variables VS.
are: 1. manipulated by researcher (i.e.
treatment type), or 2. potentially manipulable (i.e. subject matter studied.)
Attribute independent variables
are:1. fixed – not manipulated by researcher (i.
e. socioecomonic status). 2. organismic(i.e. age, gender).
3. response (i.e. scores on tests).
Causal-comparative research VS.
is:1.
ex post facto – used to explain or predict.2. uses two or more groups and one independent variable. 3. one group is a comparison group.
Correlational research
is:1. ex post facto – used to explain or predict.2. uses one group and two or more independent variables. 3. does not use a comparison group.
Ex Post Facto research
1.
after the fact. 2. explains or predicts. 3. does not manipulated the independent variable.
Descriptive survey research
1. describe phenomena as they exist. 2.
no independent or dependent variables.
Longitudinal research VS.
1. is prospective.
2. a study of the development of subjects over an extended period of time.
Cross-Sectional research
1. is retrospective. 2. studies subjects of various ages at the same point in time. 3.
relies on recollection of subjects.
Internal validity VS External validity
internal validity is the extent that what we are doing is correct. external validity is the extent to whom the results can it be generalized.
One-way ANOVA VS.
univariate statistical technique employed to compare one factor, with more than one level, on the basis of one outcome measure.
Factorial ANOVA
univariate statistical technique employed to compare two or more factors, each with more than one level, on the basis of one outcome measure.
Validity VS.
1.
is the extent to which an instrument/a test measures what it is suppose to measure.2. Validity implies reliability.
Reliability
is: 1. when an instrument/a test yields consistent results (internal consistency) 2. reliability is sometimes called “poor man’s validity”. 3. reliability does not imply validity.
Random/probability sample VS.
1. each element in the population has an equal chance/probability of being included in the sample.
2. sample is representative of the population.
Non-random/Non-probability sample
1. each element in the population does not have an equal chance/probability of being included in the sample. 2. sample not representative of the population.
Normal distribution VS.
Normal distribution is symmetrical with the mean = medium = mode.
Skewed Distribution
skewed distribution is asymmetrical with the longer tail extending away from the X and Y origin (positively skewed) or with the longer tail extending toward the x and Y origin (negatively skewed).
Statistics VS. Parameters
Statistics are indices (descriptive measures) for sample. Parameters are indices (descriptive measures) for population.
Univariate statistics VS.
Multivariate statistics
Univariate statistics have one outcome measure. Multivariate statistics have more than one outcome measure.
Simple/bivariate/zero-order partial correlation VS.
correlation between one X (independent) variable and one Y (dependent) variable.
Partial correlation
correlation between one X (independent) variable and one Y (dependent) variable, while partialling out confounding variable(s).
(i.e. r xy.z shows a first order partial correlation between X and Y, partialling out z orr xy.zw shows a second order partial correlation between X and Y, partialling out z and w.)
Multiple correlation VS. Canonical correlation
Multiple correlation is between more than one X (independent) variables and one Y (dependent) variable. Canonical correlation is between more than one X (independent) variables and more than one Y (dependent) variables.
Null hypothesis VS. Alternative/Research hypothesis
Null hypothesis (Ho)is the hypothesis of: no “difference”, no “effect”, no “relationship”. Research hypothesis (H1)is the researcher’s hunch.
Type I error VS. Type II error
Type I error is falsely rejecting a true null hypothesis. Type II error is not rejecting a false null hypothesis.
Power of the statistical test VS.
Confidence coefficient
Power is correctly rejecting a false null hypothesis (1 – Beta). Confidence coefficient is correctly not rejecting a true null hypothesis.
One-tailed/directional test VS.
Two-tailed/non-directional test
One-tailed test is when directionality is specified in the research hypothesis. Two-tailed test is when directionality is not specified in the research hypothesis.
Liberal statistical test VS.
1. more likely to find statistical significance.
2. has more power. 3.
more given to chance. 4. more likely to make Type I error. 5. less likely to make Type II error.
Conservative statistical test
1. less likely to find statistical significance. 2. has less power. 3.
less given to chance. 4. less likely to make Type I error. 5.
more likely to make Type II error.
Random selection of subjects VS.
1. identifies sample from population. 2. establishes external validity.(The sample cannot be generalized to the population without random selection.
)
Random assignment of sample
1. assigns subjects from sample to independent variables. 2. establishes internal validity.
ANCOVA VS.
1. univariate statistical technique employed to compare one factor, with more than one level, on the basis of one outcome measure, while controlling for a confounding variable (covariate).
2. confounding variable should be correlated with outcome variable.
MANOVA
multivariate statistical technique employed to distinguish among groups on the basis of their centroids from more than one outcome measure.
1. Inferential statistics infer from the sample to the population. 2. hypothesis testing and estimation are the two major branches of inferential statistics.
parametric statistics are most often used with interval or ratio (quantitative) data. non-parametric statistics are most often used with nominal (qualitative) or ordinal data.
Coefficient of determination VS.
1. true variance, explained variance. 2. denoted by r(squared).3.
it shows the proportion of variance in y explained by x).4. measure of practical significance.
Coefficient of non-determination
1.
error variance, unexplained variance. 2. denoted by 1-r(squared). 3. shows the proportion of variance in y not explained by X (i.e.
if 25% of the variance in Y is explained by X, 75% is the unexplained variance).
Measures of central tendency VS.
location of point on a distribution, (i.e. mean, mode, medium).
Measures of variability
degree of variation in a set of data, (i.
e. standard deviation, SIQR, index of dispersion, range).
Simple regression VS.
1. prediction of one outcome variable (Y) from one predictor variables (X). 2. Y is a continuous variable.
Multiple regression
1. prediction of one outcome variable (Y) from more than one predictor variables (X). 2. Y is a continuous variable.
Logistic regression VS.
1.
prediction of one outcome variable (Y) from one or more than one predictor variables (X). 2. Y is a dichotomous (binary) variable.
Discriminant analysis
multivariate statistical technique employed to describe major differences amoung groups based on their group centroids from more than one outcome measure or one categorical outcome measure with more than two levels.
Main effect VS. Interaction effect
a main effect is an outcome that is a consistent difference between levels of a factor.
an interaction effect exists when differences on one factor depend on which level you are on in another factor.
Target population VS. Accessible population
target population is the ideal/theoretical population the researcher has in mind. accessible population is the population available to the researcher.