Exam 2 Vocabulary Research Methods

descriptive statistics

a set of statistics used to organize, analyze, and summarize the properties of a set of data (data from a known sample)

data matrix

a grid presenting collected data; after collecting data, it is entered here; columns represent variables, rows represent cases

frequency distribution

a table showing how many of the cases in a batch of data scored each possible value, or range of values, on the variable; lists all possible values for the variable from lowest to highest and tallies how many participants get each score

frequency histogram

a data visualization technique showing how many of the cases in a batch of data scored each possible value, or range of values, on the variable

dot plot

a data visualization technique in which every data point for a given variable is represented; the y-axis represents all possible value, while the x-axis represents a single score/value

central tendency

a value that the individual scores in a dataset tend to center on

mode

the most common score in a dataset

bimodal

having 2 modes

multimodal

having more than 2 modes

median

the middle value in the distribution of scores in a dataset

mean

the average of all the values in a dataset; add all scores, then divide by the number of scores

variance

how spread out scores in a sample are around the mean

standard deviation

a computation that captures how far, on average, each score in a dataset is from the mean

box plot

a data visualization technique that depicts a sample’s median, interquartile range, and outliers

outlier

a score that stands out as either much higher or much lower than most other scores in the sample

z score

a computation that describes how far an individual score is above or below the mean, in standard deviation units

cohen’s d

a measure of effect size indicating how far apart two group means are in standard deviation units, telling how much overlap there is between the two sets of scores

effect size

the magnitude/size of a difference/relationship between two groups in an experiment; tells you how meaningful, important, or practical the result is

inferential statistics

a set of techniques that uses the laws of chance and probability to help researchers make decisions about what their data means and what inferences they can make from the data; using data from a sample to estimate what is happening in the population

estimation

an approach to inferential statistics that uses data from a sample to calculate an effect size and a 95% confidence interval, with the goal of predicting the magnitude of some value in a population

null hypothesis significance testing

an inferential statistics technique in which a result is compared to a hypothetical population in which there is no relationship or no difference

point estimate

a single estimate of an unknown population parameter based on sample data

confidence interval

a range of values that often contains the true population level of some variable

standard error

the typical average/error researchers make when estimating a population value; measures the amount of discrepancy that can be expected in a sample estimate vs. the true value in the population

sampling distribution of the mean

a hypothetical distribution you would get if you conducted the same study an infinite number of times and plotted the estimates you got

p value

in null-hypothesis significance testing, the probability of getting the result in a sample or one more extreme, by chance, if there is no relationship or difference in the population

null hypothesis

assuming there is no effect in the population; this is the starting point of statistics

replicable

describing a study whose results have been reproduced when the study was repeated; interrogates statistical validity

direct replication

a replication study in which researchers repeat the original study as closely as possible to see whether the original effect shows in the newly collected data; confirms what we already learned, but does not test the theory in a new context

conceptual replication

a replication study in which researchers examine the same research question but use different procedures for operationalizing the variables

replication plus extension

a replication study in which researchers replicate their original study but add variables or conditions that test additional questions

scientific literature

a series of related studies, conducted by various researchers, that have tested similar variables

review article

collects and considers all studies on a topic together, summarizing them

file drawer problem

a problem relating to literature reviews and meta-analyses based only on published literature, which may overestimate support for a theory because studies finding null effects are less likely to be published

HARKing

hypothesizing after the results are known; a questionable research practice in which researchers create an after-the-fact hypothesis about an unexpected research result, making it seem like they predicted it all along

p hacking

a family of questionable data analysis techniques, such as adding participants after the results are initially analyzed, removing outliers, or trying new analyses to obtain a p-value of just under .05, which can lead to non-replicable results

open science

the practice of sharing one’s data, hypotheses, and materials freely so others can collaborate, use, and verify results

open data

when psychologists provide their full dataset on the internet so others can reproduce the statistical results or conduct new analyses

open materials

when psychologists provide their study’s full set of measures and manipulations on the internet so others can see the full design or conduct replication studies

pre-registration

when, before collecting any data, a researcher publicly states what the study’s outcome is expected to be

null result

a study found no statistically significant results/differences between conditions

manipulation check

an extra dependent variable researchers can include to determine how well a manipulation worked

reverse design confound

when a study is designed in a way where an unintended, systematic variable works in the opposite direction of the independent variable, leading to a null result

noise

unsystematic variability among the members of a group in an experiment, which might be caused by situation noise, individual differences, or measurement error

measurement error

the degree to which the recorded measure for a participant on a variable differs from the true value of the variable for that participant

situation noise

unrelated events or distractions in the external environment that create unsystematic variability within groups in an experiment

between-subjects design

study design in which separate groups of participants are placed into different levels of the independent variable, so each participant is only exposed to one level

posttest-only design

study design in which participants, randomly assigned to independent variable groups, are tested on the dependent variable only once

pretest/posttest design

study design in which participants are randomly assigned to at least two groups and are tested on the dependent variable twice- once before and once after exposure to independent variable

within-subjects design

study design in which each participant is presented with all levels of the independent variable

repeated-measures design

study design in which participants respond to a dependent variable more than once, after exposure to each independent variable level

concurrent-measures design

study design in which participants are exposed to all levels of an independent variable at the same time, and report their preference as the dependent variable

order effects

in a within-groups design, a threat to internal validity in which exposure to one condition changes participant responses to a later condition

practice effect

a type of order effect in which participant’s performance improves over time because they become practiced at the dependent measure

fatigue effect

type of order effect in which participant’s performance degrades over time because they become tired

carryover effect

type of order effect in which some form of contamination carries over from one condition to the next

counterbalancing

in a repeated-measures experiment, presenting the levels of the independent variable to participants in different sequences to control for order effects

full counterbalancing

a method of counterbalancing in which all possible condition orders are presented

partial counterbalancing

a method of counterbalancing in which some, but not all, of the possible condition orders are presented

latin square

a formal system of partial counterbalancing to ensure that every condition in a within-subjects design appears in each position at least once

confidence interval

the probability that a population parameter will fall between a set of values

debrief

to inform participants after a study about the study’s true nature, details, and hypotheses

respect for persons

research participants should be treated as autonomous agents and certain groups deserve special protections

informed consent

right of research participants to learn about a research project, know its risks and benefits, and decide whether or not to participate

coercion

implicit or explicit suggestion that those who do not participate will suffer negative consequences

undue influence

offering an incentive to participate in a study that is too attractive to refuse

beneficence

researchers must take precautions to protect participants from harm and to promote their well-being

justice

calls for a fair balance between the kinds of people who participate in research and the kinds who benefit from it

anonymous study

research study in which identifying information is not collected, protecting identity of participants

confidential study

research study in which identifying information is collected, but is protected from disclosure to people other than researchers

institutional review board

a committee responsible for ensuring that research using human participants is ethical

deception

withholding of some details of a study from participants or actively lying to them

data fabrication

a form of research misconduct in which a researcher invents data that fits their hypothesis

data falsification

a form of research misconduct in which a researcher influences a study’s results, perhaps by deleting observations from a dataset or by influencing participants to act in the hypothesized way

plagiarism

representing the ideas or words of others as one’s own

self-plagiarism

a potentially unethical practice in which researchers recycle their own previously published text, verbatim and without attribution, in another article

replacement

animal care guideline that states researchers should find alternatives to animals in research when possible

refinement

animal care guideline that states researchers must modify experimental procedures and other aspects of animal care to minimize or eliminate animal distress

reduction

animal care guideline that states researchers should adopt experimental designs and procedures that require the fewest animal subjects possible

interaction effect

a result from a factorial design in which the difference in the levels of one independent variable changes depending on the level of the other independent variable

factorial design

a study in which there are two or more independent variables

cell

a condition in an experiment; in a simple experiment, a cell can represent the level of one independent variable; in a factorial design, a cell represents all of the possible combinations of two independent variables

participant variable

variable such as age, gender, or ethnicity whose levels are selected/measured, not manipulated

moderator

a variable that changes the relationship between two other variables, resulting in an interaction

main effect

in a factorial design, the overall effect of one independent variable on the dependent variable, averaging over the levels of the other independent variable

marginal means

the average value of a dependent variable for each level of one independent variable, averaged across all levels of other independent variables

crossover interaction

a specific type of statistical interaction in which the effects of one independent variable on a dependent variable are opposite at different levels of another independent variable

spreading interaction

a statistical interaction in factorial designs where the effect of one independent variable exists at one level of a second independent variable, but is significantly weaker or nonexistent at the other level