Advanced Research

Reliance on Authority

A way of obtaining knowledge in which people will look to authority figures for what information is correct

Intuition

A way of obtaining knowledge in which people believe information based on how they feel (Gut Feeling)

Subjective Experiences

A way of obtaining knowledge in which people believe information based on personal life experiences

Rationalism

A way of obtaining knowledge in which people believe information based on logical reasoning

Empirical Reasoning

A way of obtaining knowledge that is systematic and unbiased, based on empirical evidence

Nature is Orderly

An assumption of scientific approaches that states there is at least some form of order in the world, things do not occur randomly

Nature can be Known

An assumption of scientific approaches that states that humans are no different in nature than anything else and should be treated as such

All Natural Phenomenon Have Natural Causes

An assumption of scientific approaches that states that once empirical regularities are found, cause/effect can be established

Nothing is Self-Evident

An assumption of scientific approaches that states that claims of truth must be demonstrated objectively, and must always admit the possibility of error

Knowledge is Based on Experience

An assumption of scientific approaches that states that science has to be empirical to understand the world

Scientific Method

Step-by-Step process to address a research question

Empirical Data

Observable/Measurable Behaviors

Operational Definition

Explain variables in enough detail allow replication

Theory

Overarching belief about behavior

Hypothesis

Testable Question that addresses an aspect of a theory

Testable Hypothesis

Written in a way that allows empirical data to confirm or deny it

Falsifiable Hypothesis

Hypothesis that can be proven true or false

Variable

Anything that varies, have more than one possible value

Participants

People who recruited by researchers in research

Correlational Study

Study in which no variables are manipulated, explores relationships between variables

Block Random Assignment

A method of assignment in which each participant is randomly assigned into groups based on things such as age, gender, etc., then put to each Independent Variable level

Experiment

Study in which at least one variable is manipulated

Longitudinal Study

Type of study that collects data from people across time

Cohort

Group of participants with something in common with each other

Cross-Sectional Study

A study that collects data over several cohorts

Individual Differences

Variability across participants

Independent Variable

The variable in an experiment that is manipulated by the experimenter

Levels

Multiple areas of a variable (School Year)

Control Group

Optimal level in Independent Variable that participants are manipulated but can still complete the Dependent Variable

Dependent Variable

Outcome variable, relies on Independent Variable

Random Assignment

A method of assignment in with each participant has an equal chance of being put into each Independent Variable

Between-Subjects Design

A design of a test in which different groups of participants are exposed to different conditions

Simple Random Assignment

A method of assignment in which each participant is allocated into each Independent Variable level

Extraneous Variable

Introduces some type of variability in the data set that is not the focus of the study

Confounding Variable

A type of Extraneous Variable that changes along with the levels of the Independent Variable, causes studies to be flawed

Nuisance Variable

A type of variable that introduces random spread in the Dependent Variables values

Internal Validity

Extent to which an Independent Variable causes changes in the Dependent Variable

Levels of Measurement

Four levels that variables can be classified, determines which statistical analysis is valid

Nominal Variables

A level of measurement that shows categorical values with no real value

Ordinal Variables

A level of measurement in which categories have a meaningful order but there is no equal intervals between levels

Interval Variables

A level of measurement in which there are equal intervals between numbers on a scale but no zero point

Rating Item

Allows participants to rate response with several responses, options are discrete values with equal intervals between them

Anchors

Descriptions given to numbers

Likert Scale

Contains a range of values and labels

Sematic Differential

Terms with opposite meanings and a range of options between the terms (Rate from 1 to 5)

Visual Analogue

Participants make a mark on a continuous line

Social-Desirability Bias

Participants choose certain responses to seem more desirable

Response Bias

Participants respond to items in a systematic way while in a certain mindset

Ratio Variable

Contains levels with equal intervals with a zero point

Quantitative Data

Data that is numbers with quantity (age, height, weight)

Qualitative Data

Data that is restricted to names or categories (Colors, Ethnicity)

Variability

Spread of Values on a Data Set

Standard Deviation

Average variability around the mean of a data set

Unit of Analysis

The main entity/object that a researcher is interested in researching

Raw Score

Data collected before any manipulation takes place

Descriptive Statistics

Summaries given about raw scores (averages, variance)

Parameter

A value that describes a population

Statistic

A value that describes a sample

Inferential Statistics

Techniques for using sample data to make statements about the population the sample came from

Statistical Inference

Process of extrapolating from findings based on sample data

Test Statistic

Single number calculated from sample data that measures how much the data deviates from the null hypothesis

Statistical Significance

Indicates that an observed effect is unlikely to have happened by chance

Substantive Significance

The real-world importance of a research finding

Bivarte Statistical Tests

A statistical test involving a relationship between two variables

Multivariate Statistical Tests

Statistical Test involving the relationship between three or more variables

Effect Size

Measure of how large or meaningful a statistical result is

Measurement Error

Difference between a measured variable and the true value of the quantity being measured

Sampling Error

The difference between a true population parameter and the estimate of that parameter derived from a sample

Conditions/Treatments

Values of the independent variable

Repeated Measures Design

A design of a test that repeatedly tests the same group of participants of the same variable

Null Hypothesis

A statement that the Independent Variable has no effect on the population being tested

Alternative Hypothesis

A statement that the Independent Variable does affect the population being tested

Type I Error

An error made when you reject a true null hypothesis

Type II Error

An error made when you fail to reject a false null hypothesis

Sampling Distribution

Describes how a sample statistic differs from the population

Critical Regions

Shaded areas that contain “extreme” outcomes

Alpha

Maximum probability of making a Type I Error

One-Tailed Test

A statistical test in which the null hypothesis is rejected when going only one specific direction

Two-Tailed Test

A statistical test in which the null hypothesis is rejected if going in both directions

Statistical Power

Probability that the test will correctly reject a false null hypothesis

Parametric Tests

Statistical tests that assume a specific (normal) distribution, and use population parameters to analyze data, have much more statistical power

Non-Parametric Tests

Statistical Tests that do not assume a specific distribution, have much less statistical power

Organismic Variable

Characteristics of the subjects

Control over variables, cause/effect can be inferred

Advantages of Experimental Design

Not everything can be studied this way, difficult to put results from experiments into the real world

Disadvantages of Experimental Design

Matching vs Blocking vs Random Assignment

First Building Block of Experimental Design

Pre-Test + Post-Test vs Post-Test Only

Second Building Block of Experimental Design

One-Way vs Factorial

Third Building Block of Experimental Design

Between-Group vs Within-Group vs Mixed Factors

Fourth Building Block of Experimental Design

Generalizability

Extent to which results from a sample can be applied

Representative Sample

Sample that has the same/similar characteristics as the population

Cluster Sampling

A type of sampling in which researchers put participants into “clusters” then randomly select from them

Multistage Sampling

Type of Cluster Sampling that has 2+ stages

Stratified Random Sampling

Type of random sampling that occurs from samples of specific groups

Oversampling

Selecting a larger number of participants from underrepresented groups during Stratified Random Sampling

Confidence Sampling

Sampling the most available groups as possible

Quota Sampling

Sampling a larger number of participants from a specific group of interest

Selection Bias

Researchers choose participants in a biased way that alters the results of the experiment

Self-Selection Bias

Researchers choose any willing participants, which can affect the results

Attrition

The loss of any subjects during the study, hurts the External Validity of the study