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Rating
Ratings are summaries of behaviors or attitudes that have been organized according to a set of rules. Although the general use of the term in research can refer to written data, such as an anecdotal report, a rating usually refers to a number assigned according to a defined scale.
Ratings can be quantified, as numbers or scores chosen to identify the position on a scale, or they can be graphic indicators. Likert-type scales, for example, provide a balanced list of alternatives from which raters may choose to indicate a response to a statement or question. The response chosen is attached to a quantity that becomes a score for use in data analysis. A rating item designed in this way might appear as shown below:
The instructor for this course was prepared for each class.
- Strongly disagree
- Disagree
- Neither agree nor disagree
- Agree
- Strongly agree
This type of rating is so common that it appears intuitive, but it is not without its disadvantages. Concerns about of this method can arise when the data are analyzed statistically. Most researchers are comfortable treating these responses, and especially summed or averaged total scores across many such ratings, as interval-level measurement and apply parametric statistical methods, which assume that high level of measurement. Some critics point out that the psychological processes that lead to a rater's choosing a particular rating may not reflect an underlying interval-level scale (e.g., is the meaningful “distance” between strongly agree and agree ratings equal to the distance between disagree and strongly disagree?). So the use of parametric statistics may be inappropriate. The consensus of studies over the past several decades, however, suggests that the use of parametric statistical methods to analyze ratings of this type produces fairly accurate results, and the concern may be primarily academic.
Examples of graphic indicators as ratings include visual analog scales, which can be used to indicate with a high degree of precision a respondent's subjective impression. This method allows for a greater variability in responses and, theoretically, allows for the score to more closely estimate a true or accurate response. Pain scales, for example, ask patients to mark an “x” somewhere along a line to indicate the level of pain they are currently experiencing: No pain——————x————Most pain

The score for a graphic rating scale is determined by literally measuring the location of the mark along the line, in millimeters, for example, and that length becomes the rating. Beyond the potential for greater precision, an advantage of this rating method is that the measurement approach is more clearly interval- or ratio-level, and parametric statistical methods seem a clearly appropriate method of analysis. Concerns for this method center on the absence of many anchors, verbal description of scale points, and logistical measurement difficulties when forms are reproduced at different sizes, such as in different online contexts.
A third type of rating, the checklist, is sometimes quantified and sometimes represented as a set of independent observations. Checklists are lists of characteristics or components that may be present or absent in a person, object, or environment. A scale for depression, for example, or alcoholism, may ask people to place a checkmark next to, or circle, each characteristic that is true of them. The number of checks may be summed, and if a cut score, or critical total, is reached, then the raters may be categorized as, for example, being depressed or having a drinking problem.
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- Descriptive Statistics
- Distributions
- Graphical Displays of Data
- Hypothesis Testing
- Alternative Hypotheses
- Beta
- Critical Value
- Decision Rule
- Hypothesis
- Nondirectional Hypotheses
- Nonsignificance
- Null Hypothesis
- One-Tailed Test
- p Value
- Power
- Power Analysis
- Significance Level, Concept of
- Significance Level, Interpretation and Construction
- Significance, Statistical
- Two-Tailed Test
- Type I Error
- Type II Error
- Type III Error
- Important Publications
- “Coefficient Alpha and the Internal Structure of Tests”
- “Convergent and Discriminant Validation by the Multitrait-Multimethod Matrix”
- “Meta-Analysis of Psychotherapy Outcome Studies”
- “On the Theory of Scales of Measurement”
- “Probable Error of a Mean, The”
- “Psychometric Experiments”
- “Sequential Tests of Statistical Hypotheses”
- “Technique for the Measurement of Attitudes, A”
- “Validity”
- Aptitudes and Instructional Methods
- Doctrine of Chances, The
- Logic of Scientific Discovery, The
- Nonparametric Statistics for the Behavioral Sciences
- Probabilistic Models for Some Intelligence and Attainment Tests
- Statistical Power Analysis for the Behavioral Sciences
- Teoria Statistica Delle Classi e Calcolo Delle Probabilità
- Inferential Statistics
- Association, Measures of
- Coefficient of Concordance
- Coefficient of Variation
- Coefficients of Correlation, Alienation, and Determination
- Confidence Intervals
- Margin of Error
- Nonparametric Statistics
- Odds Ratio
- Parameters
- Parametric Statistics
- Partial Correlation
- Pearson Product-Moment Correlation Coefficient
- Polychoric Correlation Coefficient
- Q-Statistic
- R2
- Randomization Tests
- Regression Coefficient
- Semipartial Correlation Coefficient
- Spearman Rank Order Correlation
- Standard Error of Estimate
- Standard Error of the Mean
- Student's t Test
- Unbiased Estimator
- Weights
- Item Response Theory
- Mathematical Concepts
- Measurement Concepts
- Organizations
- Publishing
- Qualitative Research
- Reliability of Scores
- Research Design Concepts
- Aptitude-Treatment Interaction
- Cause and Effect
- Concomitant Variable
- Confounding
- Control Group
- Interaction
- Internet-Based Research Method
- Intervention
- Matching
- Natural Experiments
- Network Analysis
- Placebo
- Replication
- Research
- Research Design Principles
- Treatment(s)
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- Unit of Analysis
- Yoked Control Procedure
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- A Priori Monte Carlo Simulation
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- Applied Research
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- Experimental Design
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- Field Study
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- Laboratory Experiments
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- Meta-Analysis
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- Mixed Model Design
- Monte Carlo Simulation
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- Nonexperimental Design
- Observational Research
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- Pre-Experimental Designs
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- Quantitative Research
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- Repeated Measures Design
- Response Surface Design
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- Single-Blind Study
- Single-Subject Design
- Split-Plot Factorial Design
- Thought Experiments
- Time Studies
- Time-Lag Study
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- Triple-Blind Study
- True Experimental Design
- Wennberg Design
- Within-Subjects Design
- Zelen's Randomized Consent Design
- Research Ethics
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- Clinical Significance
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- Cross-Validation
- Data Cleaning
- Delphi Technique
- Evidence-Based Decision Making
- Exploratory Data Analysis
- Follow-Up
- Inference: Deductive and Inductive
- Last Observation Carried Forward
- Planning Research
- Primary Data Source
- Protocol
- Q Methodology
- Research Hypothesis
- Research Question
- Scientific Method
- Secondary Data Source
- Standardization
- Statistical Control
- Type III Error
- Wave
- Research Validity Issues
- Bias
- Critical Thinking
- Ecological Validity
- Experimenter Expectancy Effect
- External Validity
- File Drawer Problem
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- Heisenberg Effect
- Internal Validity
- John Henry Effect
- Mortality
- Multiple Treatment Interference
- Multivalued Treatment Effects
- Nonclassical Experimenter Effects
- Order Effects
- Placebo Effect
- Pretest Sensitization
- Random Assignment
- Reactive Arrangements
- Regression to the Mean
- Selection
- Sequence Effects
- Threats to Validity
- Validity of Research Conclusions
- Volunteer Bias
- White Noise
- Sampling
- Cluster Sampling
- Convenience Sampling
- Demographics
- Error
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- Criterion Problem
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- Data Mining
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- Error Rates
- Expected Value
- Fixed-Effects Models
- Inclusion Criteria
- Influence Statistics
- Influential Data Points
- Intraclass Correlation
- Latent Variable
- Likelihood Ratio Statistic
- Loglinear Models
- Main Effects
- Markov Chains
- Method Variance
- Mixed- and Random-Effects Models
- Models
- Multilevel Modeling
- Odds
- Omega Squared
- Orthogonal Comparisons
- Outlier
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- Pooled Variance
- Precision
- Quality Effects Model
- Random-Effects Models
- Regression Artifacts
- Regression Discontinuity
- Residuals
- Restriction of Range
- Robust
- Root Mean Square Error
- Rosenthal Effect
- Serial Correlation
- Shrinkage
- Simple Main Effects
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- Sums of Squares
- Statistical Procedures
- Accuracy in Parameter Estimation
- Analysis of Covariance (ANCOVA)
- Analysis of Variance (ANOVA)
- Barycentric Discriminant Analysis
- Bivariate Regression
- Bonferroni Procedure
- Bootstrapping
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- Categorical Data Analysis
- Confirmatory Factor Analysis
- Contrast Analysis
- Descriptive Discriminant Analysis
- Discriminant Analysis
- Dummy Coding
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- Estimation
- Exploratory Factor Analysis
- Greenhouse-Geisser Correction
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- Jackknife
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- Logistic Regression
- Mean Comparisons
- Missing Data, Imputation of
- Multiple Regression
- Multivariate Analysis of Variance (MANOVA)
- Pairwise Comparisons
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- Post Hoc Comparisons
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- Stepwise Regression
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- Trend Analysis
- Yates's Correction
- Statistical Tests
- Bartlett's Test
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- Chi-Square Test
- Duncan's Multiple Range Test
- Dunnett's Test
- F Test
- Fisher's Least Significant Difference Test
- Friedman Test
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- Kolmogorov-Smirnov Test
- Kruskal-Wallis Test
- Mann-Whitney U Test
- Mauchly Test
- McNemar's Test
- Multiple Comparison Tests
- Newman-Keuls Test and Tukey Test
- Omnibus Tests
- Scheffé Test
- Sign Test
- t Test, Independent Samples
- t Test, One Sample
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- Tukey's Honestly Significant Difference (HSD)
- Welch's t Test
- Wilcoxon Rank Sum Test
- z Test
- Theories, Laws, and Principles
- Bayes's Theorem
- Central Limit Theorem
- Classical Test Theory
- Correspondence Principle
- Critical Theory
- Falsifiability
- Game Theory
- Gauss-Markov Theorem
- Generalizability Theory
- Grounded Theory
- Item Response Theory
- Occam's Razor
- Paradigm
- Positivism
- Probability, Laws of
- Theory
- Theory of Attitude Measurement
- Weber-Fechner Law
- Types of Variables
- Validity of Scores
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