PSYC 523: Statistics & Research Methods Flashcards
ANOVA
- Stands for “analysis of variance”
- Statistical procedure used in inferential statistics
- ANOVAs test for significant differences among 2+ groups
- Also test for main/interaction effects of the independent variable on the dependent variable
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Clinical vs Statistical Significance
- Clinical significance that refers to when the results of a study are judged to be meaningful in relation to the diagnosis or treatments of disorders
- Statistical significance refers to the actual results of the statistical analyses that aren’t attributed to the operation of chance or random factors
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Construct Validity
- Degree to which a test or instrument is capable of measuring a concept, trait, or other theoretical entity
- The two main types of contract validity found in social science research are
- CONVERGENT validity (how well the measure correlates with other well-established measure of the same construct) and
- DISCRIMINANT validity (how much the measure does not correlate with unrelated measures)
- Tests should have BOTH convergent and discriminant validity in order to have high construct validity
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Content Validity
- Extent to which a test measures all facets of the subject matter or behavior that’s being studied
- Content validity cannot be measured empirically/statistically, but rather is assessed through logical analysis
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Correlation vs Causation
- Correlation describes the relationship (either positive or negative) between variable
- Causation refers to when changes in one variable bring about changes in the other variable(s) (i.e. cause and effect)
- A correlation is necessary to establish a causal relationship, but a correlation between variable DOES NOT assume that their is causation between the variables
- Only experimental studies can establish causal relationships
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Correlational Research
- A type of research design in which relationships between variables are simply observed without any control over the setting in which those relationships occur
- Correlational research does not contain any intentional manipulation of variables by the researcher
- The correlation coefficient can range from -1.0 to 1.0 and describes the strength and direction of the relationship (either positive or negative)
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Cross-sectional Design
- Research design in which groups, who differ by one key characteristic (i.e. age, developmental level, etc), are compared at a single point in time
- Typically used to determine the prevalence of a condition
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Dependent T-test
- Type of statistical procedure that compares the means of two related groups
- Used to determine whether or not there is significant difference between those means
Ex: relationship between “before” intervention scores vs “after interventions scores
Descriptive vs Inferential
- Descriptive statistics depict the main aspects of the sample data without inferring to a larger population (i.e. mean, median, mode, range, standard deviation within the sample)
- Inferential statistics are inferences about characteristics of a population to be drawn from a sample of data from that population, while controlling for error as much as possible
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Double-Blind Study
- Type of research design in which neither the participants nor the researchers knows which treatment/intervention participants are receiving until the study is complete
- Eliminates the possibility of researcher bias toward a participant or group
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Ecological Validity
- Refers to the degree to which results obtained from research are representative of conditions in the wider world
- Research designs with higher ecological validity are assumed to be more generalizable in life outside the confines of research/treatment
- This differs from external validity in that ecological validity describes how the results can be applied in real-life while external validity describes how results can be applied to people outside of the sample population but not necessary real life
Ex: doing a study in on the effects of alcohol consumption and then ask them to interact. To increase ecological validity they carry out the study in a bar
Effect Size
- Refers to the magnitude or meaningfulness of a relationship between two variables
- The larger the effect size, the stronger the phenomenon
- This is interpreted as indicating the practical significance of research findings
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Experimental Research
- Research design that utilizes randomized assignment of participants and a systematic manipulation of variables while all other variables controlled (or attempted to be controlled)
- The objective of experimental research is to draw a causal inference (i.e. any change in the dependent variable was due to the manipulation of the independent variable)
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Hypothesis
- An empirically testable proposition about some fact, behavior, or relationship that is usually based on theory
- The hypothesis states the expected outcome that will result from the research design’s conditions or assumptions
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Independent T-test
- Type of statistical procedure that compares the means of two independent groups
- Used to determine if the means are statistically different
Ex: “TERMS WITH EXAMPLES THE BEST” for example
Internal Consistency
- The degree of interrelationship of homogeneity among the items on a test, such that they are consistent with another and measuring for the same construct
- This is tested by dividing the items in half and scoring them separately, then correlating the town halves to check for the degree of consistency
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Internal Vaidity
- The degree to which a research study is free from flaws in its internal structure (i.e. lack of confounding variables)
- If so, this means the observed relationship between variables in the study is reflective of actual relationship between the variables
Ex: treatment for depression, didn’t allow for comorbid diagnoses so there wasn’t confounding variable. However this reduced ecological validity
Interrater Reliability
- The extent to which independent evaluators produce similar ratings from the same target person or object
- Typically used with more subjective measure to ensure the soundness of the operational definition(s) of the variable(s) in a study
Ex: “EXAMPLES BEST”
Measures of Central Tendency
- Refers to the values that attempt to describe a set of data by identifying the central position within the set of data
- The 3 main measures of central tendency ate mean, median and mode
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Measures of Variability
- how the spread of the distribution vary around the central tendency
- THREE primary measures: 1. Range 2. Variance 3. Standard Deviation
- Helps determine which statistical analysis you can run on a data set
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Nominal/Ordinal/Interval/Ratio Measurements
- Four types of scales of measure used in statistics
- Nominal measurements are used for categorical data
- Ordinal measurements are used for data that is ranked by individual or variable
- Internal measurements are used for measuring the degree of difference between values when an “absolute zero” is NOT possible
- Ratio measurements are used for measuring the degree of data when an “absolute zero” IS possible
Ex: When gathering data on a new client you ask them questions and describe their data in different terms. You ask their gender (nominal), age (interval), you give them a likert scale to rate how they feel today (ordinal) and how many panic attacks are you having a day (ratio)
Normal Curve
- Theoretical distribution in which values pile up in the center at the mean , median, and mode and falls off into “tails” at either end of the distribution
- Normal curve produces the familiar “bell-shape” when plotted
Probability
- Refers to the degree to which an event is likely to occur in a randomly sampled population
- As the probability (or p-values) increases, it becomes more likely that the result occurred due to chance as opposed to experimental manipulation
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Parametric vs nonparametric statistical analyses
- Parametric statistical analyses are based on the assumptions about the distribution of variables in the population that are being tested
- Nonparametric statistical analyses are used when the data being analyzed is does not meet the assumptions about distribution of the variables in the population
- Parametric statistics are typically preferred because they are more likely to detect statistical significance than nonparametric statistics
- Nonparametric statistics are used when the sample size is small and may not have symmetrical distribution.
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