PSYC 523

Question 1

Achievement Test

Answer 1

What: A test that is designed to measure an individual’s level of knowledge in a particular area; generally used in schools and educational settings. Often the distinction is made that achievement tests emphasize ability acquired through formal learning or training rather than their innate potential. Focuses specifically on how much a person knows about a specific topic.
Why: This is important to understand the ability one has to succeed. These tests are cost effective and their scoring is objective and reliable
EX: The comps exam is an achievement test, as it is designed to measure how thoroughly clinical counseling students have learned the information in the ten core classes of the program

Question 2

ANOVA

Answer 2

What: Analysis of Variance. A parametric statistical technique used to compare more than two experimental groups at a time. It is more flexible than a t-test because it can analyze the difference among more than two groups, even if the groups have different sample sizes.
Why: Determines whether there is a significant difference between the groups, but does not reveal where that difference lies.
EX: You are studying the effects of social media use and sleep, there is a low use and high use of social media groups. You would run an ANOVA to see if there are differences in the social media groups.

Question 3

Aptitude Test

Answer 3

What: Measures a person’s potential to learn or acquire specific skills. Aptitude tests are prone to bias. In the context of testing, this is a test designed to measure an individual’s potential for learning a specific skill. Aptitude tests rely heavily on predictive criterion validation procedures
Why: This is important for understanding a person’s innate potential. They are prone to bias.
EX: The SAT is an aptitude test designed to predict a student’s potential success in college. There is reason to doubt the predictive validity of the SAT

Question 4

Clinical vs Statistical Significance

Answer 4

What: Clinical significance refers to the meaningfulness of change in a client’s life. Statistical significance refers to the reliability of an outcome and is calculated mathematically. Generally in psychology, a result is statistically significant if the p-value is < .05, meaning that there is a less than 5% chance that the result is due to chance.
Why: Findings can be clinically significant without being statistically significant or vice versa. This is important in being a good consumer of research and understanding if the values with a study can show that an intervention is successful for a disorder.
EX: Sarah was doing research regarding if CBT was a good intervention for OCD, based on the results there was clinical significance showing that it was effective in reducing symptoms

Question 5

Construct Validity

Answer 5

What: This is the degree to which a test or instrument is capable of measuring a concept, trait, or other theoretical entity that it claims to be measuring. A variety of factors can threaten construct validity like a mismatch between the construct and its operational definition, bias, experimenter, and participants effects. There are two types: Convergent validity - does the test correlate highly with other tests that measure the concept Divergent validity - does the test correlate lowly with tests that measure different constructs.
Why: This is important in research to make sure you are actually measuring what you intended to measure. It can be determined by factor analyses.
EX: If a researcher were to create a scale measuring aggression, construct validity would be the extent to which the questions actually asked about aggression compared to assertiveness

Question 6

Content Validity

Answer 6

What: Content validity is the degree to which a measure or study includes all of the facets/aspects of the construct that it is attempting to measure. Content validity cannot be measured empirically but is rather assessed through logical analysis.
Why: This is important to research and measure the entire range of what you want to measure. This can be determined using exploratory factor analysis
EX: If a test is designed to survey arithmetic skills at a third-grade level, content validity indicates how well it represents the range of arithmetic operations possible at that level

Question 7

Correlation vs Causation

Answer 7

What: Correlation simply tells us if there is a relationship between two variables. This can be positive or negative and the coefficient will be between -1 and +1. Causation can only be concluded if there is a manipulation of an independent variable (determined via controlled studies). Correlation does not equal causation!!
Why: This is important when consuming research to find out if you are measuring just a potential relationship or if one variable causes a result in another.
EX: Marla is examining the relationship between social media use and body image. In this study she is not modifying any variable to get causation she is only assessing if the relationship exists so this is a correlational study.

Question 8

Dependent t-test

Answer 8

What: Statistical analysis that compares the means of two related groups to determine whether there is a statistically significant difference between these means. Sometimes called a correlated t-test because the data are correlated. Used when the design involves matched pairs or repeated measures, and only two conditions of the independent variable**
Why: It is called “dependent” because the subjects carry across the manipulation–they take with them personal characteristics that impact the measurement at both points—thus measurements are “dependent” on those characteristics.
EX: If you are measuring cigarette smoking habits in specific smokers before and after an intervention, you would use a dependent t-test to measure habits on the same group before and after intervention.

Question 9

Descriptive vs Inferential

Answer 9

What: Descriptive statistics are used to describe or summarize data but do not tell about differences or relationships. This includes mean, median, mode, standard deviation, etc. Inferential statistics are used to make inferences about the probability or extent of a relationship/difference. Instead of just summarizing the data, they summarize the relationships found in the data set. There are parametric and non-parametric inferential statistics. They are used to test hypotheses and if conclusions drawn from a sample can be generalized to a population.
Why:
EX: A researcher conducts a study examining the rates of test anxiety in Ivy League students. This is a descriptive study because it is concerned with a specific population. However, this study cannot be generalized to represent all college students, so it is not an inferential study

Question 10

Effect Size

Answer 10

What: A quantitative measure of the strength of a relationship between two variables; refers to the magnitude of an effect. This can be measured using Cohen’s d, r-squared in correlation, which will show the number of standard deviations units between two means. Effect size can be used with the correlation between two variables, regression coefficients or the mean difference.
Why: It is also valuable for quantifying the effectiveness of a particular intervention, relative to some comparison - commonly used in Meta-analyses. Often, effect sizes are interpreted as indicating the practical significance of a research finding.
EX: A researcher conducts a correlational research study on the relationship between caffeine and anxiety ratings. The study produces a correlation coefficient of 0.8 which is considered a large effect size. The effect size reflects a strong relationship between caffeine and anxiety.

Question 11

Independent T-test

Answer 11

What: Used to determine if there are significant differences between two group means. This is used when there are two conditions of the independent variable to determine if there are differences between groups using group means. The independent t-test is specifically used when the two groups are not related to each other.
Why: We make the assumption that if randomly selected from the same population, the groups will mimic each other; the null hypothesis is no difference between the two groups
EX: Gabi is researching gender differences in the use of CBT for depression. She will run an independent t-test to understand if there are differences in effectiveness between groups

Question 12

Internal Consistency

Answer 12

What: this type of reliability refers to the extent to which different items on a test measure the same ability or trait. In other words, internal consistency measures whether several items that propose to measure the same general construct produce similar scores and are free from error. Internal consistency is usually measured with Cronbach’s alpha; measured using split-half in which both halves are correlated or by using the reliability coefficient - ranges from 0-1.Internal consistency is an index of the reliability of a test.
Why: It is the degree of interrelationship or homogeneity among items on a test and it is important to measure what you are supposed to be measuring.
EX: Donna is creating a questionnaire to assess the Big 5 personality traits, she uses internal consistency to make sure the items are measuring what they are supposed to be

Question 13

Internal Validity

Answer 13

What: The extent to which the observed relationship between variables in a study reflects the actual relationship between the variables. Control for confounding variables can increase internal validity, as well as a random selection of participants. Thus, internal validity is how sure we can be that the experimental treatment was the only cause of change in a dependent variable(s). It pertains to the soundness of results obtained within the controlled conditions of a particular study, specifically with respect to whether one can draw reasonable conclusions about cause-and-effect relationships among variables
Why:
EX: Researchers investigated a new tx for depression using tight controls in terms of who could be a participant. For instance, they did not allow anyone with comorbidity to participate. This increased the study’s internal validity. It did, however, jeopardize the ecological validity of the research.

Question 14

Interrater Reliability

Answer 14

What: A type of reliability that measures the agreement level between independent raters. Useful with measures that are less objective and more subjective. The extent to which independent evaluators produce similar ratings in judging the same abilities or characteristics in the same target person or object. It can be expressed using a correlation coefficient.
Why: Used to account for human error in the form of distractibility, misinterpretation or simply differences in opinion.
EX: Gabi is running a study on CBT for depression, she has three of her professors assess the reliability of the questionnaire and finds that they get the same reliability. It can be assumed that her study is reliable.

Question 15

Measures of Central Tendency

Answer 15

What: Provides a statistical description of the center of the distribution, and describes a data set. Three main measures are used: the mean, mode and median.
Mean is the arithmetic average of all scores within a data set.
Mode is the most frequently occurring score.
Median is the point that separates the distribution into two equal halves.
Why:
EX: Sarah works at a school and wants to see the norm scores for a math exam the whole school took. She will use the mean of the combined scores to figure out that information

Question 16

Measures of Variability

Answer 16

What: measures of variability are how the spread of the distribution varies around the central tendency. Three primary measures: range, variance and standard deviation.
Range is obtained by taking the two most extreme scores and subtracting the lowest from the highest.
Variance is the average squared deviation around the mean and must be squared because the sum of the variations would equal zero.
Standard deviation is the square root of the variance and is highly useful in describing variability.
Why: It is important to see the outliers of data to assess if they need to be dropped to get accurate data when running tests. Helps determine which statistical analyses you can run on a data set
EX: Sarah had the entire third grade take a math test to see their current math abilities. After running standard deviation, she was able to see outliers and who was struggling or excelling in this subject.

Question 17

Normal/Ordinal/Interval/Ratio Measurements

Answer 17

What: These are all levels of measurement of variables, which all vary in the degree of precision to which they can be recorded. The level of measurement is important because it determines the type of statistical analyses that can be run with the data set.
Nominal data: dichotomous, only two levels, such as male and female, or categorical, such as Republican, Democrat, Independent. They have none of the 3 properties that distinguish scales
Ordinal data (numbers) indicate order only (1st born, 2nd born)
Interval data: true score data where you know the score a person made and you can tell the actual distance between individuals based on their respective scores, but the measure used to generate the score has no true zero (temperature, F or C, SAT scores)
Ratio data: interval data with a true zero (age, height, weight, speed)
Why: Both nominal and ordinal data are non-continuous, while interval and ratio are continuous.
EX: Gabi is running a study to assess happiness in grad level students. Using an ordinal scale she able to assess who is the least happiest to the most

Question 18

Norm-Referenced Scoring/Tests

Answer 18

What: A norm referenced test evaluates a test taker’s performance against a standardized sample; typically used for the purpose of making comparisons with a larger group. Norms should be current, relevant, and representative of the group to which the individual is being compared.
Why: There are some issues with norms, many norming samples attempt to be representative of the population which can result in several categories being represented by very few people. This can lead to inappropriate scoring, or test acceptability with some populations and has resulted in within group norming.
EX: The child psychologist tested the adolescent’s IQ and discovered that the child’s IQ was 165, placing him in the 99th percentile, more than 3 SDs above the mean on the normal curve because IQ is normally distributed. IQ testing is an example of norm referenced scoring/testing because an individual’s score is always interpreted in terms of typical performance/results.

Question 19

Normal Curve

Answer 19

What: A normal curve is a normal distribution, graphically represented by a bell-shaped curve. A frequency where most occurrences take place in the middle of the distribution and taper off on either side. The population tends to carry a symmetrical distribution with most behaviors. Within 1 SD on either side of the median, mode and mean lies 68% of the data.
Why: Many statistical models are based on the assumption that data follow a normal distribution.
EX: A researcher is developing a new intelligence test. After obtaining the results, they found that the scores fell along a normal curve: most participants scored in the middle range with very few obtaining either the highest or lowest scores (scores were normally distributed).

Question 20

Objective Tests

Answer 20

What: This is a type of psychological assessment instrument consisting of a set of items that have specific correct answers, such that no interpretation, judgment, or personal impressions are involved in scoring. For example, yes/no, true/false. They are scored quantitatively and not influenced by rater variables. Unambiguous stimuli and answers are scored quantitatively. Not open to interpretation; there is a correct and a wrong answer
Why:
EX: Gabi is a teacher in a special education classroom and wants to assess the math skills of her class. She will create a test that each question only has one answer to see who is understanding the material

Question 21

Probabilty

Answer 21

What: A mathematical statement indicating the likelihood that something will happen when a particular population is randomly sampled, symbolized by (p). The chance that we have found something interesting is not due to chance or error. A p-value of .05 is generally accepted; this means that there is a 95% chance the IV influenced the DV.
Why: The higher the p value, the more likely that the phenomenon or event happened by chance
EX: Gabi wants to understand the probability that a child from poverty will grow up to have a drug addiction, she will distribute a test and assess it using a p-value to see if poverty influences potential drug addiction.

Question 22

Projective Tests

Answer 22

What: The general idea behind projective tests is that a person’s interpretation of an ambiguous stimulus reflects his unique characteristics; most often personality tests. Based on a projective hypothesis that says when people attempt to understand an ambiguous or vague stimulus, their interpretation of that stimulus reflects their needs, feelings, experiences, thought processes.
Why: TAT and Rorschach tests are generally used and require extensive training. These tests need to be interpreted so they are always more at risk for error.
EX: You are seeing a client and you ask them to interpret a black ‘blob’ while using the Rorschach inkblot test. This is a projective test that suggests the client saying that she sees a crab in the image might be indicative of her mood at the time of testing.

Question 23

Parametric vs Nonparametric Statistical Analyses

Answer 23

What: Parametric statistics are ones that are based on a normal distribution, have homogeneity of the variance, and require data to meet certain assumptions. Nonparametric statistics are statistics that use non-continuous data or collapse continuous data into a non-continuous distribution to examine relationships. There are no assumptions made and are less robust, typically only used when necessary. You would use a chi-squared test to get these statistics. They are used for nominal /ordinal data and skewed interval/ratio.
Why: Parametric analyses are preferred because they have greater statistical power and are more likely to detect statistical significance. This is important to understand when running tests on data.
EX: Professor Johnson handed out a survey to students to assess preferences of exams on a like to dislike scale. This is ordinal data therefore requiring nonparametric statistics to be run

Question 24

Regression

Answer 24

What: Regression is a prediction based on correlated data. Correlation tells us whether a relationship exists. Regression allows us to predict based on that relationship by identifying the line of best fit. This is generally assessed using R-squared to describe, explain, or predict the variance of an outcome or dependent variable using scores on one or more predictor or independent variables. Linear regression is predicting one variable to another while multiple regression uses multiple predictor variables.
Why: This is important to assess if there is a relationship between X and Y. It produces a line of best fit. The stronger the correlation, the less error in prediction.
EX: A developmental psychologist performed a study on aggressive behavior in boys and hormone levels. Researchers performed a regression analysis on the data. Their results showed that the severity and frequency of the boys’ aggression could be accurately predicted based on the levels of testosterone

Question 25

Reliability (types)

Answer 25

What: It is a measure of the trustworthiness or consistency of a measure. The degree to which the instrument is free of random error, yielding the same results across multiple applications. This is used to assess how reliable a testing method is to get the same results over different conditions and free of measurement error.
Internal reliability is the extent to which a measure is consistent within itself (split half, KR20, Alpha) while external reliability is the extent to which a measure varies from one use to another (interrater, test-retest, and parallel forms).
Test-Retest Reliability examines consistency of a measure from one time to another. Same test given at two points in time. Correlation between those scores obtained by the same person on 2 occasions
Inter-Rater Reliability examines the degree of consistency between different raters’ scores. Correlation between those scores
Parallel Forms Reliability examines the consistency of the results of two tests constructed in the same way from the same content domain. Tests must be very very similar! Correlation between the equivalent forms of the test
Why:
EX: A teacher wants to assess if the kids in her class are learning the math material she is teaching. She will test them on the material and then again 3 weeks later. This will help assess if the measure is consistent.

Question 26

Sample vs Population

Answer 26

What: A sample is a relatively small subset of the population that is selected to represent the population in a study; the sample must be representative of the population being studied. A population is all members of a group; the larger group of individuals from which a sample is selected. Random sampling is the primary method for obtaining samples and is the best way to mimic the population from which the samples were drawn
Why: It’s important to ensure that the sample is representative of the population in research because it increases the potential that any findings of importance can be generalized back to the whole population
EX: A researcher is studying the effects of CBT on depression. Out of the population of depressed people, the researcher will get a random sample of this population

Question 27

Standard Error of Estimate

Answer 27

rd error of estimate:
What: This is part of regression and is the relationship between X and Y given by a regression equation as an index of how closely the predicted value of y for a specific value of x matches its actual value. The smaller the standard error of estimate, the more confident one can be in the accuracy of the estimated y value. How much the data points are spread around the regression line.
Why:
EX: A researcher finds that the standard error of the estimate for measuring depression scores is only .001 away from the true value of the sample’s depression. This is a good standard error.

Question 28

Standard Error of Measurement

Answer 28

What: In simpler terms- an estimate of how much an individual’s score would be expected to change if retested with the same/equivalent form of test. The smaller the SEM the more precise the measurement capacity of the instrument. The larger the SEM, the greater the score variation across administrations
Why: The SEM provides an indication of how confident one may be that an individual’s obtained score on any given measurement opportunity represents their true score.
EX: An athlete takes a concussion test every year. Each time it is administered to get a baseline measurement, the athlete scores within the same 2 points. The standard error of measurement is low

Question 29

Standard Error of the Difference (2 sample t-test)

Answer 29

What: Statistical calculation that informs, on average, how much deviation there is across groups in a study. Scores from the two groups are paired up, and the difference between each pair is calculated. These differences provide a distribution of deviations, of which the average is standardized giving the standard error of the difference. It’s the estimate of error between the two groups. A two-sample t-test compares the means of two samples to see if they came from the same population.
Why:
EX: Anxiety between teenagers in the US and Germany. A researcher conducts a study on how caffeine affects test scores. They take the mean of scores from each group (with or without caffeine) and calculate the differences between the means. They then used the standard error of the difference to find the amount of error between the estimated and actual difference.

Question 30

Test Bias

Answer 30

What: In the context of psychometrics; a systematic error in the measurement process that differentially influences scores for identified groups. OR. This is the tendency of scores on a test to systematically over or underestimate the true performance of individuals to whom that test is administered, particularly because they are members of specific groups like ethnic minorities or genders.
Why: This bias is a systematic error and is important to keep in mind when adding cultural and ethnic factors into test making.
EX: Researchers develop a test that examines depression levels. The test uses language and vernacular that is not easily recognized by non-white American populations. The test has bias.

Question 31

Type 1 and Type 2 Error

Answer 31

What: A type I error is rejecting the null hypothesis when it is in fact true. It is detecting an effect or relationship that does not actually exist. “A false positive.” The probability of making a type I error is the alpha or significance level p-value. Type II error is failing to reject the null hypothesis when it is in fact not true. This is not detecting an effect or relationship exists when there actually is one. This can be assessed using the beta level
Why:
EX: A researcher is studying CBT for depression and gets a p-value of .06 and says that there were significant differences in the groups. This would be a type I error as there was not an effect discovered

Question 32

Validity (types of)

Answer 32

What: The extent to which you’re measuring the construct you intended to measure; in general a validity coefficient of 0.3-0.4 is considered adequate.
Content Validity: degree to which a measure represents all aspects of a given construct ; how well a measure encompasses the full domain of what it is trying to measure
Criterion Validity: extend to which the test corresponds with a particular criterion against which it is compared; how well one measure predicts outcome of another measure
Concurrent Validity: extent to which a new measure correlates with a previously established/validated measure
Construct Validity: the degree to which the test measures the construct or trait it intends to measure
Face Validity: a logical rather than statistical quality; the extent to which a test is subjectively viewed as covering the concept it purports to measure In the context of research…are results trustworthy and meaningful?
Internal Validity: whether the effects observed in a study are due to the manipulation of the independent variable and not some other factor
External Validity: the extent to which the results of a study can be generalized to other situations and to other people
Ecological validity, an aspect of external validity, refers to whether a study’s findings can be generalized to the real world
Why:
EX: A researcher is coming up with a depression measure. She compares the validity with the BDI and it turns out to be valid, she is using concurrent validity.

Question 33

Variance

Answer 33

What: This is the measure of the spread, or dispersion, of scores within a sample or population, whereby a small variance indicates highly similar scores, all close to the sample mean, and a large variance indicates more scores at a greater distance from the mean and possibly spread over a larger range. A measure of variability; the average squared deviation around the mean
Why: Variance is helpful in research because it can be quantified using statistics and converted to a number that can be used to compare between samples or across samples in populations to see which has the most or least variance or to see how much variance may change due to an intervention or treatment applied.
EX: A researcher is studying the effects of an SSRI on depression symptoms. The variance between the placebo group and the intervention group is high. This means that the SSRI works to treat depressive symptoms.