Statistics (Research & Data Analysis in Psychology) Exam 1 2023 with verified questions and answers

independent variable has at least two levels that we either manipulate or observe (quasi independent) to determine its effects on the dependent variable - participants in each level are thought to either display or be exposed to the conditions of this variable in a consistent manner - ex: caffeine vs. no caffeine, gender (quasi independent) dependent variable the outcome variable that we hypothesized to be related to, or caused by, changes in the independent variable - dependent variables are only in experimental studies quasi-independent variable the variable that has been manipulated, though there was no random assignment into groups quasi-dependent variable the variable that we think was impacted by the quasi-independent variable characteristics of an ideal experiment 1. the participants in each of your conditions (groups) are the same 2. all conditions go through the same procedure, except for what you are manipulating 3. sample is representative of population 4. reliable and valid measure of DV randomization assigning participant to conditions with no visible pattern - the best way to assign participants to an experimental condition matched sample match conditions (groups) based on particular characteristics (e.g. age, income, gender) - useful if true randomization isn't possible experimenter bias if the experimenter is aware of the hypothesis and knows which condition he/she is in, he or she might bias the experiment by acting different in one condition (e.g. smiling more) than in other conditions - the individual may not be aware of their biased actions descriptive statistics information about a sample of everyone of interest in the study - summarizes and describes your data - ex: mean, standard deviation, range - ex: the average grade on the test was 85.4 out of 100 inferential statistics information about a population based on information from a smaller set of information - uses the results of your data to make predictions or generalize about a larger population - ex: dancers have a higher IQ than golfers; private school graduates earn more than public school graduates reliability consistency in measurement - your measure gives the same result even if measured at different times, in different ways inter-rater reliability consistency in scores between observers/measurers test-retest reliability consistency in measurements across dimensions of the test split half reliability testing reliability by creating equal halves of a test and determining the overlap between each individual's scores parallel forms reliability creating two different equivalent tests and determining the overlap between each individual's scores - if Gade created two exams and had you take both, you'd score about the same on both of the exams validity accuracy of measurement with respect to intent - the degree to which the study accurately answers the question that is was intended to answer - the ability for a variable obtained to accurately reflect the concept of interest - your measure is measuring what you want it to measure face validity does the measurement appear to be effective for the aims of the study - aka construct validity predictive validity does the measure predict related behaviors/measures - ex: aggression questionnaire predicting aggressive activity concurrent validity does the measure relate to previously established measures of the same variable - aggression questionnaire versus old aggression questionnaire internal validity validity established if the study produces a single, unambiguous explanation for the relationship between variables - sometimes has issues with third/confounding variables external validity validity established if the study's results can be generalized to the population of interest - issues with bias confounding/third/extraneous variables variables that are in a study that are not part of the hypothesized relationship - can cause relationships to emerge - can remove a relationship - can strengthen a relationship - internal validity issue solutions to internal validity issues standardize your presentation to subjects - attempt to control for subject differences when assigning conditions generalizability the extent that sample performance represents that of the population selection biasing greater selection probability of specific individuals representativeness issues available samples might not be representative of the population - ex: college student effect volunteer bias specific individuals might be more apt to volunteering responses - ex: Kinsey effect species issues ethical concerns might make human research necessary, but how generalizable are the results to the population? experiment design issues real-world applications - sample's prior exposure effects (rats running in previous experiments) reliability and validity you can have reliability without validity - you cannot have a valid measurement without it being reliable - if you're measuring the same variable, it's reliability - if you're looking at the relationship between two different variables, it's validity nominal/categorical variables variables that have no numerical meaning - values are categories - ex: religion (atheist = 1, christian = 2, jewish = 3) - ex: gender, favorite ice cream - qualitative numerical/quantitative variables variables that are numbers - the levels of these variables that are represented as numbers - ex: rank order, interval, ratio, averages, and other arithmetic transformations make sense ordinal variables variables that have that have a natural order, but the precise distance between values is not defined - type of quantitative variable - ex: grade levels, rank in school, age groups interval variables variables that have values where the distance between them is meaningful and consistent - no true zero - type of quantitative variable - ex: IQ scores, temperature in Fahrenheit, Likert responses ratio variables interval variables where there is a true zero and where ratios of values make sense - type of quantitative variable - ex: income, height, temperature in Kelvin population a complete set of people, events, or scores that we're interested in parameter the measurable characteristic of the population that is of interest sample a subset or portion of that population statistic the measurable characteristic of the sample of the population that we're interested in - a measure of some attribute of a sample - samples can be one element or a large collection of elements why not just test a population size, time, money/expense, and ethical issues what do I need to worry about with my sample? is it representative of the population? - is my sample large enough? statistics the science of collecting, analyzing, and interpreting data variables characteristics or conditions that change in values from individuals or situations - often arbitrary (e.g. happiness scales) - measurements of some variables are not perfectly consistent correlation relationship comparison difference change influence target population the group of individuals that are interested in studying accessible population the group of individuals that have access to in your attempts to conduct an experiment - time, location, availability representative sample an experiment sample that represents the target population biased sample a sample with characteristics that are different from the target population assignment techniques methods of distributing participants into different groups of a study random assignment creating groups by giving each participant an equal chance of being in the experimental conditions/levels convenient assignment assignment of individuals based on experimenter discretion - this can be very bad if it's a biased convenient assignment sampling bias a method of selection that increases the likelihood of a biased sample - ex: grabbing your friends - availability bias (taking who will volunteer) - intended characteristics bias (smart women, dumb men example) probability sampling selecting individuals for a research study sample that is based on knowledge of all of the members of a population, and an attempt to randomly equal chance of each member of the population being picked non-probability sampling sampling that is done when the population is not known and the sampling method is based on factors not related to chance convenience sampling sampling based on the availability of participants quota sampling the assembled sample has the same proportions of individuals as the entire population with respect to known characteristics, traits, or focused phenomenon - sampling based on availability, with the inclusion on restrictions that the sample be based on population or intended/statistical proportions subjective probability probability derived from an opinion of the likelihood of an event occurring representative heuristic our tendency to guess the likelihood of something based on if it resembles what we think it should look like