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178 Cards in this Set

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Confirmation bias
The human tendency to seek our information that confirms what is already believed
Research
The principal method acquiring knowledge and uncovering the causes for behavior.
Scientific Explanations
A tentative explanation for a phenomenon, based on objective observation and logic, and subject to empirical test.
Parimonious Explanations
An explanation or theory that explains a relationship using relatively few assumptions.
Commonsense Explanations
Loose explanations for behavior that are based on what be believe to be true about the world.
Belief-based explanations
An explanation for behavior that is accepted without evidence because it comes from a trusted source or fits within a larger framework or belief
Pseudoexplantions
An explanation proposed for a phenomenon that simply relabels the phenomenon without really explaining it.
Circular Explanations (Tautology)
An explanation of behavior that refers to factors whose only proof of existence is that behavior they are being called on to explain
Method of authority
Relying on authoritative sources (for example, books, journals, scholars) for information.
Rational Method
Developing explanations through a process of deductive reasoning
Scientific Method
The method of inquiry preferred by scientists. It involves observing phenomena, developing hypotheses, empirically testing the hypotheses, and refining and revising hypotheses.
Variable
A characteristic or property that varies in amount or kind, and can be measured (eg height, weight)
Hypothesis
A tentative statement, subject to empirical test, about the expected relationship between variables.
Basic Research
Research carried out primarily to test a theory or empirical issues.
Applied Research
Research carried out to investigate a real-world problem
Deductive reasoning
Reasoning that goes from the general to the specific. Forms the foundation of the rational method of inquiry.
Theory
A set of assumptions about the causes for behavior and the rules that specify how the causes operate. A theory is subjected to empirical test and retained, modified, or rejected.
Empirical question
A question that can be answered through objective observation.
Operational definition
A definition of a variable in terms of the operations used to measure it.
Scientific Theory
A theory that goes beyond simple hypothesis, deals with verifiable phenomena, and is highly ordered and structured.
Law
A relationship that has been substantially verified through empirical test
Model
Specific application of a general theoretical view. The term model is sometimes used as a synonym for theory.
Mechanisitic explanation
An explanation for a phenomenon given in terms of a mechanism that is assumed to produce it through an explicit chain of cause and effect
Functional explanation
An explanation for a phenomenon given in terms of its function, that is, what it accomplishes.
Quantitative theory
A theory in which terms are expressed mathematically rather than verbally.
Qualitative theory
A theory in which terms are expressed verbally rather than mathematically.
Descriptive theory
A theory that simply describes the relationship among variables without attempting to explain the relationship.
Analogical theory
A theory that explains a relationship through analogy to a well-understood theory
Fundamental theory
A theory that proposes a new structure or underlying process to explain how variables and constants relate.
Domain
The range of situations to which a theory applies. Also called the scope of a theory.
Confirmational strategy
A strategy for testing a theory that involves finding evidence that confirms the predictions made by the theory.
Disconfirmational strategy
A method of testing a theory that involves conducting research to provide evidence that disconfirms that predictions made by the theory.
Literature review
A review of relevant research and theory conducted during the early stages of the research process to identify important variables and accepted methods and to establish a rationale for research hypotheses.
Primary source
A reference source that contains the original, full report of a study. It includes all the details needed to replicate and interpret the study.
Secondary source
A reference source that summarizes information from a primary source and includes research reviews and theoretical articles.
Referred Journal
A journal whose articles have undergone prepublication editorial review by a panel of experts in the relevant field
Non-referred journal
A journal in which articles do not undergo prepublication editorial review
Paper session
A meeting at a scientific convention at which the most up-to-date research results are presented. A paper session may involve disseminating data by reading a paper or presenting a poster
Personal communication
Information obtained privately from another researcher (for example, by letter or phone.)
Psych Info
A computerized database system that indexes journals and book chapters relevant to psychology and related fields
Psycharticles
A computerized source of articles, downloadable in PDF format, that were published in the journals of the American Psychological Association
Causal relationships
A relationship in which changes to the value of one variable cause changes in the value of another.
Correlational relationship
A relationship in which the value of one variable changes systematically with the value of a second variable.
Correlational research
Research in which no independent variables are manipulated. Instead, two or more dependent variables are measured to identify possible correlational relationships
Thrid variable problem
A problem that interferes with drawing causal inferences from correlational results. A third, unmeasured variable affects both measured variables, causing the latter to appear correlated even though neither variable influences the other
Directionlity problem
A problem that interferes with drawing causal inferences from correlational results that involves not being able to clearly specify the direction of causality between variables.
Experimental research
Research in which independent variables are manipulated and behavior is measured while extraneous variables are controlled
Independent variable
The variable that is manipulated in an experiment. Its value is determined by the experimenter, not the
subject.
or the variable whose effect is being studied
Treatment
A level of an independent variable applied during an experiment. In multifactor designs, a specific combination of the levels of each factor
Dependent variable
The variable measured in a study. Its value is determined by the behavior of the subject and may depend on the value of the independent variable
or the variable expected to change due to variations in the independent variable
Experimental group
A group of subjects in an experiment that receives the nonzero level of the independent variable.
Control group
A group of subjects in an experiment that does not receive the experimental treatment. The data from the control group are used as a baseline against which data from the experimental group are compared.
Extraneous variable
Any variable that is not systematically manipulated in an experiment but that still may affect the behavior being observed
Demonstration
A nonexperimental technique in which some phenomenon is demonstrated. No control group is used.
Internal validity
The extent to which a study evaluates the intended hypotheses
Confounding
Two variables that vary together in such a way that the effects of one cannot be separated from the effects of the other
External validity
The extent to which the results of a study extend beyond the limited sample used in the study.
Simulation
A laboratory research technique in which you attempt to re-create as closely as possible a real-world phenomenon
Test-rest reliability
A method of assessing the reliability of a questionnaire by administering repeatedly the same or parallel form of a test.
Parallel-forms relaibility
Establishing the reliability of a questionnaire by administering parallel (alternate) forms of the questionnaire repeatedly.
Split-halif reliability
A method of assessing reliability of a questionnaire using a single administration of the instrument. The questionnaire is split into two parts, and responses from the two parts are correlated
Accuracy
Agreement of measurement with a known standard.
Validity
The extent to which a measuring instrument measures what it was designed to measure.
Face Validity
How well a test appears to measure (judging by its contents) what it was designed to measure. Example: a measure of mathematical ability would have face validity if it contained math problems.
Content Validity
Validity of a test established by judging how adequately the test samples behavior representative of the universe of behaviors the test was designed to sample.
Criterion-related Validity
The ability of a measure to produce results similar to those provided by other, established measures of the same variable
Concurrent Validity
The validity of a test established by showing that its results can be used to infer an individual’s value on some other, accepted test administered at the same time.
Nominal Scale
A measurement scale that involves categorizing cases into two or more distinct categories. This scale yields the least information.
Ordinal Scale
A measurement scale in which cases are ordered along some dimension (for example, large, medium, or small). The distances between scale values are unknown
Interval Scale
A measurement scale in which the spacing between values along the scale is known. The zero point of an interval scale is arbitrary.
Ratio scale
Highest scale of measurement; it has all of the characteristics of an interval scale plus an absolute zero point.
Range effects
A problem in which a variable being observed reaches an upper limit (ceiling effect) or lower limit (floor effect).
Behavioural measure
A measure of a subject’s activity in a situation; for example, the number of times a rat presses a level (frequency of responding).
Physiological measure
A measure of a bodily function of subjects in a study (for example, heart rate).
Self-report measure
A measure that requires participants to report on their past, present, or future behavior.
Predictive Validity
The ability of a measure to predict some future behavior.
Construct Validity
Validity that applies when a test is designed to measure a “construct” or variable “constructed” to describe or explain behavior on the basis of theory (for example, intelligence). A test has construct validity if the measured values of the construct predict behavior as expected from the theory (for example, those with higher intelligence scores achieve higher grades in school).
Q-sort methodology
A qualitative measurement technique that involves establishing evaluative categories and sorting items into those categories
Demand characteristics
Cues inadvertently provided by the researcher or research context concerning the purposes of a study or the behavior expected from participants.
Role attitude cues
Unintended cues in an experiment that suggest to the participants how they are expected to behave.
Experimenter bias
When the behavior of the researcher influences the results of a study. Experimenter bias stems from two sources: expectancy effects and uneven treatment of subjects across treatments.
or
due to his or her expectations, the experimenter might inadvertently treat groups of subjects differently.
Expectancy effects
When a researcher’s preconceived ideas about how subjects should behave are subtly communicated to subjects and, in turn, affect the subjects’ behavior.
Single-blind
The person testing subjects in a study is kept unaware of the hypotheses being tested.
Double-blind
Neither the participants in a study nor the person carrying out the study know at the time of testing which treatment the participant is receiving.
Manipulation checks
Measures included in an experiment to test the effectiveness of the independent variables.
Population
All possible individuals making up a group of interest in a study. For example, all U.S. women constitute a population. A small proportion of the population is selected for inclusion in a study (see sample).
Sample
A relatively small number of individuals drawn from a population for inclusion in a study. See also population.
Generalization
Applying a finding beyond the limited situation in which it was observed
Random sample
A sample drawn from the population such that every member of the population has an equal opportunity to be included in the sample.
Nonrandom sample
A specialized sample of subjects used in a study who are not randomly chosen from a population.
Institutional review board (IRB)
A committee that screens proposals for research using human participants for adherence to ethical standards
Volunteer Bias
Bias in a sample that results from using volunteer participants exclusively
Deception
A research technique in which participants are misinformed about the true nature and purpose of a study. Deception is ethical if the researcher can demonstrate that important results cannot be obtained in any other way.
Role playing
Alternative to deceptive research that involves having participants act as though they had been exposed to a certain treatment
Debriefing
A session, conducted after an experimental session, in which participants are informed of any deception used and the reasons for the deception
Institutional animal care and use committee (IACUC)
A committee that screens proposals for research using animal subjects and monitors using institutional animal-care facilities to ensure compliance with all local, state, and federal laws governing animal care and use.
Behavioral categories
The general and specific classes of behavior to be observed in an observational study.
Cohen's Kappa
A popular statistic used to assess interrater reliability. It compares the observed proportion of agreement to the proportion of agreement that would be expected if agreement occurred purely by chance.
Intraclass correlation coefficient
A measure of agreement between observers that can be used when your observations are scaled on an interval or ratio scale of measurement
Qualitative data
Data in which the values of a variable differ in kind (quality) rather than in amount
Naturalistic observation
Observational research technique in which subjects are observed in their natural environments. The observers remain unobtrusive so that they do not interfere with the natural behaviors of the subjects being observed.
Ethnography
A nonquantitative technique used to study and describe the functioning of cultures through a study of social interactions and expressions between people and groups.
Participant observation
An observational research technique in which a researcher insinuates him- or herself into a group to be studied
Nonparticipant observation
An observational research technique in which the observer attends group functions and records observations without participating in the group’s activities.
Sociometry
A nonexperimental research technique involving identifying and measuring interpersonal relationships within a group
Interrater reliability
The degree to which multiple observers agree in their classification of quantification of behaviour.
Case history
A nonexperimental research technique in which an individual case is studied intensively to uncover its history (for example, a patient in therapy).
Archival research
A nonexperimental research strategy in which you make use of existing records as your basic source for data.
Content analysis
A nonexperimental research technique that is used to analyze a written or spoken record for the occurrence of specific categories of events
Meta-analysis
A statistics-based method of reviewing literature in a field that involves comparing or combining the results of related studies. See also traditional literature review.
Inferential statistics
Statistical procedures used to infer a characteristic of a population based on certain properties of a sample drawn from that population
Standard error of the mean
An estimate of the amount of variability in expected sample means across a series of samples. It provides an estimate of the deviation between a sample mean and the underlying population mean.
Degrees of freedom (df)
The number of scores that are free to vary in a distribution of a given size having a known mean.
Type I error
Deciding to reject the null hypothesis when, in fact, the null hypothesis is true. Also referred to as an alpha error.
Type II error
Deciding not to reject the null hypothesis when, in fact, the null hypothesis is false. Also referred to as a beta error
Alpha level
The probability of obtaining a difference at least as large as the one actually obtained, given that the difference occurred purely as a result of chance factors. By convention, the maximum acceptable alpha level of .05 (5 chances in 100 or 1 change in 20).
Critical region
Portion of the sample distribution of a statistic within which observed values of the statistic are considered to be statistically significant. Usually the 5 percent of cases found in the upper and/or lower tail(s) of the distribution.
t test
An inferential statistic used to evaluate the reliability of a difference between two means. Versions exist for between-subjects and within-subjects designs and for evaluating a difference between a sample mean and a population mean.
t test for independent samples
A parametric inferential statistic used to compare the means of two independent, random samples in order to assess the probability that the two samples came from populations having the same mean.
t-test for correlated samples
A parametric inferential statistic used to compare the means of two samples in a matched-pairs or within-subjects design in order to assess the probability that the two samples came from populations having the same mean
z test for the difference between two proportions
A parametric inferential statistic used to determine theprobability that two independent, random samples came from populations having the same proportion of “successes” (for example, persons favoring a particular candidate).
Analysis of varians (ANOVA)
An inferential statistic used to evaluate data from experiments with more than two levels of an independent variable or data from multifactor experiments. Versions are available for between-subjects and within-subjects designs
F ratio
The test statistic computed when using an analysis of variance. It is the ratio of the between-groups variance and within-groups variance.
P value
In a statistical test, the probability, estimated from the data, that an observed difference in sample values arose through sampling error. p must be less than or equal to the chosen alpha level for the difference to be statistically significant
Planned comparisons
Hypothesis-directed statistical tests made after finding statistical significance with an overall statistical test (such as ANOVA).
Unplanned comparisons
Comparison between means that is not directed by your hypothesis and is made after finding statistical significance with an overall statistical test (such as ANOVA).
Per-comparison error
The alpha level for each of any multiple comparisons made among means.
Familywise error
The likelihood of making at least one Type I error across a number of comparisons
Analysis of covariance (ANCOVA)
Variant of the analysis of variance used to analyze data from experiments that include a correlational variable (covariate).
Chi-square
Nonparametric inferential statistic used to evaluate the relationship between variables measured on a nominal scale
Mann-Whitney U test
Nonparametric inferential statistic used to evaluate data from a two-group experiment in which the dependent variable was measured along at least an ordinal scale. It can also be used on interval or ratio data if the data do not meet the assumptions of the t test for independent samples.
Wilcoxon signed ranks test
A nonparametric statistical test that can be used when the assumptions of the t test for correlated samples are seriously violated
Power
The ability of an experimental design or inferential statistic to detect an effect of a variable when one is present
Effect size
The amount by which a given experimental manipulation changes the value of the dependent variable in the population, expressed in standard deviation units.
Data transformation
Mathematical operation applied to raw data, such as taking the square root or arcsine of the original scores in a distribution. Often applied to data that violate the assumptions of parametric statistical tests, to help them meet those assumptions.
Characteristics of a GOOD theory
1.Can account for the data collected
2.Has explanatory relevance (logical soundness)
3.Is testable
4.Predicts novel events
5.Is parimonious
There are 5 of them
What are the stepts in research?
1.Ask a question
2.Make preliminary observations (start to formulate an hypothesis)
3.Make predictions from hypothesis that can easily be tested empirically
4.Indentify variables that need to be measured; define problem ie cat.of beh: operational/ostensive, choose appropriate research design
5.Choose suitable research environment
6.Collect sufficient date and make sure you have enough subjects in order to validate and invalidate your hypothesis
7.Use the appropriae statistical data analysis (exploratory or confirmatory data anaylsis, then back to step 1
Hypothesis - characteristics
1. asking questions
2. hypothetico-deductive scienc (experiemental psych.)
3.tentative explanation- oftern includes a statement about the relp. between two or more variables
4. should be testable
Steps in developing theories
1. Defining the scope of the theory
2. Reviewing the literature
3. Formulating the theory
4. Establishing predictive validity
5. Testing the theory empirically
Characteristics of a GOOD theory
1.Can account for the data collected
2.Has explanatory relevance (logical soundness)
3.Is testable
4.Predicts novel events
5.Is parimonious
There are 5 of them
What are the steps in research?
1.Ask a question
2.Make preliminary observations (start to formulate an hypothesis)
3.Make predictions from hypothesis that can easily be tested empirically
4.Indentify variables that need to be measured; define problem ie cat.of beh: operational/ostensive, choose appropriate research design
5.Choose suitable research environment
6.Collect sufficient data and make sure you have enough subjects in order to validate and invalidate your hypothesis
7.Use the appropriae statistical data analysis (exploratory or confirmatory data anaylsis, then back to step 1
Hypothesis - characteristics
1. asking questions
2. hypothetico-deductive scienc (experiemental psych.)
3.tentative explanation- oftern includes a statement about the relp. between two or more variables
4. should be testable
Steps in developing theories
1. Defining the scope of the theory
2. Reviewing the literature
3. Formulating the theory
4. Establishing predictive validity
5. Testing the theory empirically
What is the (main) difference between deductive and inductive reasoning?
Deductive-Test theories
Inductive-creates theories
Why use signal detection theory?
1. originally used so that we could learn why people make different decisions based on the same information. (i.e radar)
2. **able to deal with uncertaincy
3. noise is oftern the cause of the uncertaincy
1. yes+Simulus present-hit
2. yes+Nostimulus - false alarm
3. no+Stimulus -miss
4. no- noStimulus present - correct rejection
5.d' discrimination, b-beta
Discrimination d'
the recognition that it is between stimulus one or stimulus two (ie. Gadbois trying to figure out the difference between a Coyote and Wolfe)
signal
the stimulus, object, target
Noise
(ie flicker paradigm) the uncertaincey factor, the inferrence (intrinsic and extrinsic)
Response
Response-bias
- the action or decision
- the basis of the action or decision made
Global Accuracy
-subjects is right with "hit" and "CR"
What type of reasearch design is used most often in Pharmacology?
Within subjects design. If you are introducting a new dose you must wean people off first then introduce new dose. Better than between because you can test effects on one individual, we all metabolize drugs differently.
How is "sensitivity" lost?
occurs during changs in experimentation. use behavioural tests which are most sensitive ie. the fins and gills of fish change color, posture due to disorientation.
What is specifity?
Problems finding out which effects ie GABA or something else, produce what behaviour.
ex. if you give fish either GABA or Cora antagonist you can determine whether their behaviour is specific to the antagonist
Accuracy is a function of systematic error or bias. How can error or bias be prevented?
1. observer blinding
2.Subjects blinding or unconsious
3. Instrument calibration
there are 3
What is random error?
Fluctuations which are normal in experimentation and there is not much you can do to prevent them. The best thing u can do is to increase the number of subjects and/or observations. ex. subjects may be told not to eat before the experiment, but it is possible that they did eat before the experiment without you knowing.
How can you maintain reliability or consistency in experimentation?
1. Test-rest
2.Split-half
3.Alternate(parallel) forms
How do you assess precision?
-measures of varability
-standard deviation (sd of repeated measures)
-coefficient of variation X/C *100
-measures of concordence (correlation coeficient)
-other tools
-
What is intra and Inter observer?
Intra=within - evaluating the consistency of the observer in measurement or proceedure: most often occur with long-term projects

Inter=between - making sure to do everything consistently when doing a task (not necessarily over time)
What are the types of validity?
-face
-content
-Criterion
-External(related to ecological)
there are 4 1/2 types
What is an experimental science?
Astronomy is a science, that does not use experiments. It is largely based on observations (observational science)
- Experimental science is able to create controlled environments
-very much about trying to explain the cause of a behaviour
Is Psychology an observational science?
No- in psychology we usually have a theory, then test theory, opposite of observational science.
What are the problems with observational science?
no controlled environments
what is/are the characteristics of good science?
-there is a constant loop
-utilizes both observational and experimental science
- A good scientist is analytical and generalist
Ethology
Science based mostly on observations
Small n research
- When each subject(s)is in a seperate part of the experiment
*uses within subject design
- When making comparisons between subjects
* reliability is assessed by replication (eliminates bad data patterns caused by fautly subjects)
- convinent (easier to find a smaller number of subjects)
PDP
using neural networks to test thoeries, explains how learning happens
Baseline study
AB design -each subjects becomes their own control (no contorl group) ie. GSR (lie dector)
Reversal Designs
AB <--> A-B
a treatment after administering it ie. treatment-reversal-treatment
-pooling fallacy must be avoided
-must be careful not to put too many variables on one subject
pooling fallacy
The pooling fallacy: problems arising. when individuals contribute more than one observation to the data set.
Things to consider when looking at baseline studies.
- consistent level of magnitude of treatment effects
- consistent trend (uni-directional trend)
Stability - consistent level or constistent trend. how stable is the trend over time. If trend changes this may suggest there is a carry-over affect
search of temporal patterns: tiems seires ie seasonal data, prementral syndrom, SAD
-Sequential analysis
Sequential analysis
interested in very discrete events. Similar to temporal data. How often or in what patterns do these events occur? ie. couples who argue, who does what first? men first, women respond, must then break pattern
What is a time-series model and what is the disadvantage of the time-series model?
A set of ordered observations on a quantitative characteristic of an individual or collective phenomenon taken at different points in time. Usually the observations are successive and equally spaced in time.
Disadvantage: it causes you to take in alot of date over a long period of time.
carry-over effect
A carryover effect is an effect that "carries over" from one experimental condition to another. Whenever subjects perform in more than one condition here is a possibility of carryover effects. For example, consider an experiment on the effect of rate of presentation on memory. Subjects are presented with a list of words and asked to recall as many words as they can. In one condition, the words are presented one word per second; in the other condition, the words are presented two words per second. The question is whether or not having performed in one condition affects performance in the second condition.
Hawthorne effect
The effecdt that being observed has on behaviour
a demand characteristic
Descriptive statistics
concerned with organizing, describing, quantifying, and summarizing a collection of actual observations
Inferential statistics
researchers generalize beyond actual observations. Concerned with making an inference from the sample involved in the research to the population of interest and to provide an estimate of popular characteristics
Standard deviation
provides a measure of the typical distance of scores from the mean.
Variance
is simply the square of the standard deviation and is a description of how much each score varies from teh mean.