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exam 202 #2
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Independent Variable (IV)
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The variable that is manipulated or categorized to observe its effect on the dependent variable.
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Dependent Variable (DV)
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The outcome or response measured in the study; it depends on the manipulation of the independent variable.
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1
Independent Variable (IV)
The variable that is manipulated or categorized to observe its effect on the dependent variable.
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2
Dependent Variable (DV)
The outcome or response measured in the study; it depends on the manipulation of the independent variable.
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3
Levels of IVs
Different variations or categories of the independent variable.
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4
Confounded Variables
Variables that are unintentionally related to both the independent and dependent variables, making it difficult to draw clear conclusions.
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5
Measured Variables
Variables that are observed or recorded without intervention (e.g., height, mood).
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6
Manipulated Variables
Variables that are intentionally altered in an experiment (e.g., dosage of a drug).
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7
Quantitative Variables
Variables that represent a measurable quantity (e.g., height, weight, time).
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8
Categorical Variables
Variables that categorize or classify data into distinct groups (e.g., gender, type of therapy).
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9
Continuous Variables
Variables that can take any value within a range (e.g., temperature, time).
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10
Discrete Variables
Variables that have distinct, separate values (e.g., number of children, number of cars).
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11
Nominal Scale
Categorical data with no order (e.g., gender, colors).
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12
Ordinal Scale
Categorical data with a meaningful order but no fixed intervals between categories (e.g., rankings).
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13
Interval Scale
Numeric data with equal intervals but no true zero (e.g., temperature in Celsius).
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14
Ratio Scale
Numeric data with equal intervals and a true zero (e.g., weight, height).
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15
Reliability
The consistency or stability of a measure.
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16
Validity
The degree to which a measure assesses what it claims to measure.
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17
Face Validity
Whether a measure appears to measure what it's supposed to measure.
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18
Content Validity
Whether the measure covers all aspects of the concept being measured.
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19
Criterion Validity
Whether a measure predicts an outcome it should theoretically predict.
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20
Concurrent Validity
The measure's correlation with a relevant outcome at the same time.
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21
Predictive Validity
The ability of the measure to predict future outcomes.
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22
Error Bars
Lines indicating the variability of data in a graph, usually representing the standard deviation or confidence interval.
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23
Histograms
A bar graph for continuous data, showing frequency distribution.
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24
Pie Chart
A circular chart divided into slices to represent proportions of a whole.
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25
Scattergrams (Scatter Plots)
A graph that shows the relationship between two continuous variables.
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Line Graphs
A graph that shows data points connected by lines, often used for showing trends over time.
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27
Internal Validity
The extent to which the study accurately measures the relationship between the IV and DV, without influence from confounding variables.
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28
Construct Validity
The extent to which a measure truly measures the concept it intends to measure.
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29
External Validity
The extent to which the findings of a study can be generalized to other contexts, populations, or settings.
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30
Statistical Validity
The degree to which the statistical analysis used in the study is accurate and appropriate.
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31
Ecological Validity
The extent to which the findings of a study can be generalized to real-world settings.
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32
Sample Size
The number of participants or observations in a study. Larger sample sizes typically yield more reliable results.
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33
Variance
A measure of how much scores in a dataset differ from the mean.
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Effect Size
The magnitude of the relationship or difference between variables, showing how meaningful a result is.
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35
p-values
A statistical measure that helps determine the significance of the results.
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