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EPPP-Statistics and Research Design
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Terms in this set (80)
Cluster Sampling
In contrast to other forms of sampling (which involve selecting individuals from the population), this entails selecting units or groups (clusters) of individuals from the population (e.g., schools, hospitals, clinics).
Experimental Research
This involves conducting a study to test hypotheses about the relationship between independent and dependent variables.
True Experimental
This permits greater control over the experimental situation - its "hallmark" is random assignment to groups.
Quasi-Experimental
This permits less control.
Independent Variable
This is manipulated in a research study for the purpose of determining its effects on the dependent variable; the variable that is believed to have an effect on the dependent variable. Must always have at least two levels.
Depenent Variable
This is observed and measured in a study and is believed to be affected by the independent variable.
Random Assignment
Refers to a method of assigning subjects to treatment groups using a random method; sometimes referred to as "randomization." Considered the "hallmark" of true experimental research because it enables an investigator to conclude that an observed effect of an IV is due to the IV rather than error.
Random Error
This is unpredictable and sampling error is a type of this.
Systematic Error
This is predictable error.
Extranious Variables
Also known as confounding variables and are a source of systematic error.
Demand Characteristics
Cues in the experimental situation that inform research participants of how they are expected to behave during the course of the study. These can threaten a study's internal and external validity.
External Validity
The degree to which a study's results can be generalized to other people, setting, conditions, etc.
Internal Validity
The degree to which a study allows an investigator to conclude that observed variability in a dependent variable is due to the independent variable rather than to other factors.
Maturation
A threat to internal validity. It occurs when a physical or psychological process or event occurs as the result of the passage of time (e.g., fatigue, loss of motivation) and has a systematic effect on subjects' status on the dependent variable.
History
This refers to an event that is external to a research study and that is not relevant to the research hypothesis but that affects subjects' performance on the dependent variable in a systematic way and thereby confounds the results of the study.
Selection
This threatens internal validity when participants in different treatment groups are initially different and, therefore, would differ at the end of the study even if no treatment had been applied. This is a threat when participants are not randomly assigned.
Internal Recording
A method of behavioral sampling that involves dividing a period of time into discrete intervals and recording whether the behavior occurs in each interval. It is useful for behaviors that have no clear beginning or end.
Event Sampling
A method of behavioral sampling that is useful for behaviors that are rare or that leave a permanent product. It involves recording each occurrence of a behavior during a predefined or preselected event.
Protocol Analysis
Technique used by cognitive psychologists to identify the cognition underlying problem-solving and decision-making. Involves having an individual "think aloud" while working and then analyzing the record (protocol) of the individual's verbalizations.
Between Groups Designs
Studies in which the effects of the different levels of one ore more IV's are compared by administering each level or combination of levels to a different group of participants.
Mixed Design
Research designs in which both between-groups and within-subjects comparisons can be made.
Single-Subject Designs
Contain at least one A (baseline) and one B (treatment) phase and include multiple measurements of the DV at regular intervals during each phase.
Each subject acts as a no treatment control
AP Design
Includes a single baseline (A) phase and single treatment (B) phase.
Reversal Design
This includes, at a minimum, two baseline phases and one treatment phase (e.g., an ABA or ABAB design). The treatment is WITHDRAWN during the second and subsequent baseline phases.
provide additional control over the study's internal validity. . helps exclude possibility that the change is due to a historical event or other extraneous variable.
Multiple Baseline Design
Unlike reversal (AKA WITHDRAWAL DESING), Involves sequentially applying a treatment to different "baselines" (e.g., to different behaviors, settings, or subjects). Ince behavior is applied it is not withdrawn.
Within-Subjects Design
An experimental design in which each participant receives, at different times, each level of the IV (or combination of the IVs) so that comparisons on the DV are made within participants rather than between groups.
Scales of Measurement
A method of categorizing the various ways to measure variables. There are four scales of measurement that differ in terms of mathematical "sophistication." From least to to most sophisticated they are nominal, ordinal, interval, and ratio. A nominal scale yields "frequency data"; that is, the frequency of observation in each nominal category. Ordinal, interval, and ratio scales yield scale values and scores.
Central Limit Theorom
This is derived from probability theory that predicts that the sampling distribution of the mean (1) will approach a normal shape as the sample size increased, regardless of the shape of the population distribution of scores; (2) has a mean equal to the population mean; and (3) has a standard deviation equal to the population standard deviation divided by the square root of the sample size.
Mean
This is the arithmetic average of a set of scores. It can be used when scores are measured on an interval or ratio scale.
Median
This is the middle score in a distribution of scores when scores have been ordered from lowest to highest. It is used with ordinal data (and often with interval and ratio data when the distribution is skewed or contains one or a few outliers).
Mode
This is the value (score or category) that occurs most frequently in a distribution of nominal categories.
Normal Curve
A symmetrical bell-shaped distribution that is defined by a specific mathematical formula. When scores on a variable are normally-distributed, it is possible to conclude that a specific number of observations fall within certain areas of that distribution that are defined by the standard deviation: In a normal distribution about 68% of observations fall between the scores that are plus and minus one standard deviation from the mean, 95% between the scores that are plus and minus two standard deviations from the mean, and 99% between the scores that are plus and minus three standard deviations from the mean.
Standard Deviation
A measure of dispersion (variability) of scores around the mean of the distribution. Calculated by dividing the sum of the squared deviation scores by N (or N - 1) and taking the square root of the result. The square root of the variance.
Alpha (Level of Signifcance)
This refers to the probability of rejecting the null hypothesis when it is true; i.e., the probability of making a Type I error. The value of this is set by an experimenter prior to collecting or analyzing the data. In psychological research, it is usually set at either .01 or .05.
Main Effect
The effect of a single IV on the DV.
Interaction
This occurs when the impact of an IV differs at different levels of another IV.
Factorial Design
The name given to any research design that includes two or more "factors" (independent variables). This permits analysis of main and interaction effects.
Null Hypothesis
This is stated in a way that implies that the independent variable does not have an effect on the dependent variable.
Alternative Hypothesis
This is expressed in a way that implies that the independent variable does have an effect.
Rejection
This region of a sampling distribution contains those sample values (e.g., means) that are unlikely to be obtained simply as the result of sampling error. When an inferential statistical test indicates that the obtained sample value falls in this region, the null hypothesis is rejected and the alternative hypothesis is retained. The size of the rejection is defined by alpha.
Retention
This region is the region of a sampling distribution that contains those values that are likely to be obtained simply as the result of sampling error. When an inferential statistical test indicates that an obtained sample value is in the retention region, the null hypothesis is retained and the alternative hypothesis is rejected. The retention region is equal to one minus alpha.
Sampling Distribution of the Mean/Standard Error of the Mean
The distribution of sample means that would be obtained if an infinite number of equal-size samples were randomly selected from the population and the mean for each sample calculated. This is normally shaped, its means is equal to the population mean, and its standard deviation is equal to the population standard deviation divided by the square root of the sample size. Used in inferential statistics to determine how likely it is to obtain a particular sample mean given the population mean, the population standard deviation, the sample size, and the level of significance.
Skewed Distributions
Asymmetrical distributions in which the majority of scores are located on one side of the distribution. In a positively skewed distribution, most scores are in the low side but a few scores are in the high (positive) side of the distribution. In a negatively skewed distribution, the majority of scores are in the high side of the distribution, but a few are in the low (negative) side. (Remember, it's the "tail that tells the tale"!)
Type I Errors
This occurs when a true null hypothesis is rejected. The probability of making this is equal to alpha.
Type II Errors
This occurs when a false null hypothesis is retained. The probability of making this is equal to beta (which is usually unknown).
ANCOVA (Analysis of Covariance)
A version of ANOVA used to increase the efficiency of the analysis by statistically removing variability in the DV that is due to an extraneous variable. When using this, each person's score on the DV is adjusted on the basis of his or her score on the extraneous variable.
Autocorrelation
A disadvantage of the time-series and other within-subjects designs is that the analysis of the data can be confounded by this, which occurs when subjects' performance on the post-tests is likely to correlate with their performance on the pretests. This can inflate the value of the inferential statistics (e.g. t or F), thereby resulting in an increased probability of a Type I error.
Chi-Square Tests
Inferential statistical tests used when the data to be analyzed represent a nominal scale.
Single-Sample Chi-Square Test
This is used when the study includes one variable.
Multiple-Sample Chi-Square Test
This is used when it includes two or more variables.
Experimentwise Error Rate
Refers to the probability of making a Type I error. As the number of statistical comparisons in a study increases, this rate also increases.
Factorial ANOVA
The type of ANOVA used when a study includes two ore more IVs. Also referred to as a two-way ANOVA, there-way ANOVA, etc., with the words "two" and "three" referring to the number of IVs.
MANOVA
A form of the ANOVA used when a study includes one or more IVs and two or more DVs, each of which is measured on an interval or ratio scale. Use of this helps reduce the experimentwise error rate and increases power by analyzing the effects of the IV(s) on all DVs simultaneously.
One-Way ANOVA
A parametric statistical test used to compare the means of two or more groups when a study includes one IV and one DV that is measured on an interval or ratio scale. It is preferable to multiple t-tests when a study involves more than there groups because it helps control experimentwise error rate. This yields an F-ratio that indicates if any group means are significantly different. The F-ratio represents a measure of treatment effects plus error divided by a measure of error only (MSB/MSW). When the treatment has an effect, the F-ratio is larger than 1.0.
Parametric Tests
These are inferential statistical test that are used when the data to be analyzed represent and interval or ratio scale and when certain assumption about the population distribution(s) are met: i.e., when scores on the variable of interest are normally distributed and when there is homoscedasticity (population variances are equal). These are more "powerful".
Nonparametric Tests
These are inferential statistical tests used when the data is represented by either nominal or ordinal scale ore when the assumption of the parametric test have not been met. Include chi-square tests, the Mann-Whitney U, and the Wilcoxon matched-pairs test.
Randomized Blcok Factorial ANOVA
A version of the ANOVA that is appropriate when blocking has been used as a method for controlling an extraneous variable. Allows investigator to statistically analyze the main and interaction effects of the extraneous variable (which is being treated as an additional IV).
Statistical Power
Refers to the probability of rejecting a false null hypothesis. It cannot be directly controlled but can be increased by including a large sample, maximizing the effects of the IV, increasing the size of alpha, and reducing error.
T-Tests
Parametric test used to compare two means.
Single Sample T-Tests
This is used to compare a single obtained sample mean to a known or hypothesized population mean.
Independent Samples T-Test
This is used to compare means from two independent samples.
Correlated Samples T-Test
This is used to compare two sample means when subjects in the two groups are related in some way (e.g., because they are matched on an extraneous variable or because a single-group pretest/postest design was used).
Trend Anaylsis
A type of analysis of variance used to assess linear and nonlinear trends when the IV is quantitative.
Correlation Coefficient
This is a numerical index of the relationship (degree of association) between two or more variables. The magnitude of the coefficient indicates the strength of the relationship; its sign indicates the direction (positive or negative).
Pearson r
This is used when data on both variables represent a continuous scale.
Point Biserial
This is used when one variable is a true dichotomy and the other is continuous.
Biserial
This is used when one variable is an artificial dichotomy and the other is continuous.
Eta
This is used when the variables are continuous but have a nonlinear relationship.
Counterbalanced Design
A research design used to control carryover (order) effects; involves administering the different levels of the IV to different subjects or groups of substances in a different. order. The Latin square design is a type of this.
Cross-Validation/Shrinkage
Refers to validating a correlation coefficient (e.g., a criterion-related validity coefficient) on a new sample. Because the same chance factors operating in the original sample are not operating in the subsequent sample, the correlation coefficient tends to "shrink" on cross-validation. In terms of the multiple correlation coefficient (R), shrinkage is greatest when the original sample is small and the number of predictors is large.
Discriminate Function Analysis
The multivariate technique used when there are two or more continuous predictors and one discrete (nominal) criterion. Referred to as multiple discriminant function analysis when the criterion has more than one category.
LISREL
A causal (structural equation) modeling technique used to verify a predefined causal model or theory. More complex than a path analysis; it allows two-way (non-recursive) paths and takes into account observed variables, the latent traits they are believed to measure, and the effects of measurement error.
Multiple Regression
The multivariate technique used for predicting a score on a continuous criterion based on performance on two ore more continuous and/or discrete predictors. Ideally, predictors included in this will have low correlations with each other and high correlations with the criterion. (High correlations between predictors is referred to as "multicollinearity.") The output of this is the multiple correlation coefficient and a multiple regression equations.
Path Analysis
A causal modeling technique used to verify a per-defined causal model or theory. Involves translating the theory into a path diagram, collecting data on the variables of interest (the observed variables), and calculating and interpreting path coefficients.
Regression Analysis
A statistical technique used to predict the score on a criterion based on the person's obtained score on a predictor. Involves the identification of a regression line ("line of best fit") and the use of the equation for that line, the regression equation.
Shared Variability
A correlation coefficient can be squared to obtain a measure of shared variability. For example, if the correlation between X and Y is .50, this means that 25% of variability in Y is shared with (or is accounted for by) variability in X.
Choosing a Measure of Central Tendency
Nominal Mode
Ordinal Mode or Median
Interval Mode, Median, Mean
Ratio Mode, Median, Mean
The Relationship Between the Measures of Central Tendency in Skewed Distributions
In a positively skewed distribution, hump closer to the left, the mean is greater than the median which, in turn, is greater than the mode. In a negatively skewed distribution, the relationship between the three measures is reversed: The mode is greater than the median, which is greater than the mean.
Single group time-series design
subjects at as no treatment controls. The dv is measured several times at regular intervals
shortcoming
threatened by history
does help control maturation as maturational effects tend to occur gradually over time and can usually be detected in the overall pattern of pre and post test scores.
When assessing reliability and validity of a job selection test, your sample includes current employees who are doing fairly well on the job. The nature of your sample will have which of the following effects: inflate or deflate reliability and validity coefficients.
It will deflate the reliability coefficient because your subject sample will show a tighter correlation.
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