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Midterm

# PSCH 242 Midterm: Psych 242 Exam 3 Study Guide Premium

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Department
Psychology
Course
PSCH 242
Professor
Gobel Eric
Semester
Spring

Description
242-15 Reliability of a Measure (Ch. 5: p. 129-135) ● Define and describe the concept of reliability of a measure, including its relationship to the term random measurement error. Reliability - consistent results when the measurement is repeated under the identical conditions. Small random error = more reliable measure. If a measurement is reliable, consistent results ensure that any small, random errors that could be occurring while the measure is being taken are minimal. ● Describe three types of reliability: test-retest reliability, inter-rater reliability, and internal reliability. Identify when it is appropriate to use each of these types of reliability. Test-Retest Reliability - consistent results after every remeasure Interrater Reliability - consistent results no matter who is observing Internal Reliability - consistent results no matter how you ask ● Explain how to visually present reliability data with scatterplots, and how to quantify reliability data with a Pearson r correlation coefficient or percent agreement. Explain how to apply this general approach to each of the three types of reliability: ● test-retest reliability ■ r ≥ .50 → good test-retest reliability (seen on scatterplot w/ Pearson r) ■ Correlate 1st measurements with 2nd measurements. ● inter-rater reliability ■ r ≥ .70 → good interrater reliability (seen on two scatterplots w/ two Pearson r’s) ■ Using percent agreement for categorical data → 70-80% is good interrater reliability. ■ Correlate one observer’s measurements with other’s. ● internal reliability with Cronbach's alpha ■ Alpha is calculated from mean of all inter-item correlations which you’ll see from their questionnaire responses. ■ ⍺ ≥ 0.70 → good internal reliability ● Explain two potential problems when evaluating test-retest reliability and how they would distort the r-value. 1. Respondents remembering their earlier responses (inflates r). 2. Respondents changing between their administrations (deflates r). ● Be able to identify and interpret evidence for reliability in journal articles. **eek, we’ll see about that** ● Define and describe the concept of accuracy of a measure, including its relationship to the term measurement bias, for which claims accuracy is most important, and how it can be assessed. Briefly explain how accuracy might apply to psychological measures (e.g., standardizing measures that do not use standard units). Accuracy - produces results that agree with a known standard. Measurement Bias - the average measured value systematically differs from the true value because of ‘bias’ error. Smaller measurement bias = more accurate measure. This is because, the less bias the researcher or subject has, the more likely the value can turn out to be close to the true value. Can be assessed by: measuring a known standard with the instrument and comparing values. Accuracy is not relevant when measures are NOT using standard units because the values measured CANNOT be compared to a standardized value. (AKA no reason for accuracy). ● Differentiate between reliability and accuracy of a measure (e.g., how a reliable instrument can still be inaccurate due to measurement bias), and explain why reliable measures are essential. Reliability is referring to how close one’s measured values are to each other while accuracy is referring to how close one’s measured values are to a true, known, standard value. **A measure should always be reliable. W/o reliability, single measurements will vary unpredictably from true values even if the measurement is accurate.** 242-16 Construct Validity (Ch. 5: p. 136-150) ● Define and describe the concept of construct validity and why it is important. Differentiate construct validity from reliability and accuracy, using examples such as phrenology. Construct Validity - is the operational definition a good measure/manipulation of the interested variable. Reliability & Accuracy are properties of the measure itself while construct validity depends on the conceptual variable. Need to take into consideration both reliability and accuracy before construct validity. ❖ Example: Phrenology ~ studying one’s personalities based off head measurements. ■ Valid measure of head size ■ Invalid measure of intelligence ● Define and describe two subjective ways of assessing construct validity: face validity and content validity. Face Validity - it looks like a plausible measure of the conceptual variable. Content Validity - it includes all the parts that the theory says it should contain. ● Define criterion validity and explain how it can be empirically assessed using correlation coefficient evidence and known-groups evidence for criterion validity. Distinguish between concurrent and predictive criterion validity. Criterion Validity - whether the measure is related to a relevant, concrete outcome. Two Types: ➔ Concurrent Validity - the data collected at the same time predict each other’s behavior. ➔ Predictive Validity - the measure predicts a future outcome. Empirically Assessed by using: a. Correlation Coefficients - when an outcome is quantitative, scatterplots and correlation coefficients can be used to tell whether the two variables measured correlate. b. Known-Groups - when the outcome is categorical, tables or bar graphs can be used to distinguish if there is a correlation between variables. ● Explain how convergent validity and discriminant validity can be used together to establish construct validity of a measure. Convergent Validity - the measure correlates strongly with other validated measures of the same construct. Discriminant Validity - the measure correlates less strongly with validated measures of different constructs. ● Describe the relationship between reliability and construct validity of a measure. Explain and give examples of how a measure can be reliable yet invalid for a particular conceptual variable, and explain why it doesn't make sense to talk about construct validity of an unreliable measure. A measure can be reliable but not valid. Ex: Phrenology and measuring head size to determine intelligence level. This is reliable measure for head size but invalid for intelligence. NOTE: A measure cannot be valid without being reliable because if a measure isn’t correlated with itself (reliable), it cannot be correlated with anything else (validity). ● Be able to identify and interpret evidence for construct validity in empirical journal articles. **eh, we’ll see** 242-17 Sampling and External Validity (Ch. 7: p. 181-193; Video) ● Define and describe the concept of external validity and why it is important. Define and differentiate between two types of external validity: population validity and ecological validity. External Validity = Generalization Scientists often wish to generalize findings to other people and contexts (ex. External validity) Generalizing to other people not included in the study. Population Validity - do the research findings obtained from a sample apply to the population of interest? Generalizing to other contexts and situations beyond those studied. Ecological Validity - do the research findings reflect what people do in the real world? May be influenced by subject reactivity, the research setting, or how variables are operationalized. ● Define the terms population of interest, census, and sample. Population of Interest - all individuals to whom we desire to generalize the findings of a research study May be defined in many ways All adult humans All native english speakers All children in day care Children in daycare in Chicago Census - includes every member of a population Sample - smaller subgroup of subjects chosen from the population ● Differentiate between a representative sample and a biased (unrepresentative) sample, and relate these terms to the concept of population validity. Explain why a given sampling technique would be likely to result in a representative or biased sample. Representative Sample - closely match the various characteristics of the population of interest. Biased Sample - occur when sample characteristics don’t match those of the population. Usually stems from nonrandom sampling procedures Attempting to generalize outside a biased sample may be misleading or inaccurate. ● Describe the two major steps in the selection process for acquiring a final sample (for a study with informed consent). Define the concepts of sampling bias and non-response or volunteer bias and explain how they can affect the final sample and population validity of a research study. Two Major Steps: 1. Recruitment (risk for sampling bias) ● Only part of target population will be accessible (sampling frame) ● May not reflect population of interest (convenience sampling) ● Reach potential participants in some way (contacted sample) so they are aware of possibility to participate. 2. Enrollment (risk for non-responsive or volunteer bias) ● inclusion/exclusion criteria ● Eligible participants choose to participate or decline to participate ● Self-selection for participation ● Define and differentiate between probability or random sampling and a nonrandom sampling. Random Sampling - every member of the population of interest has an equal chance of being chosen. Usually generates a representative sample, so findings can be generalized to the population from which it was drawn. Nonrandom Sampling - every member of the population of interest does not have the same chance of being chosen. Will be a biased or unrepresentative sample, so must be cautious in generalizing findings to the intended population. ● Explain how to employ various probability sampling techniques (simple random sampling, proportionate stratified random s
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