"Unit Zero" - Research Design

Hypothesis

tentative explanation - must be FALSIFIABLE - able to be supported or rejected

Operational Definition

clear, precise, quantifiable definition of your variables -

Hows replication and collection of reliable data

Qualitative data:

descriptive data (eye color)

Quantitative data

numerical data - IDEAL and necessary for statistics

Population

everyone the research could apply to

Sample

the people ( or person ) specifically chosen for your study

Correlation

identify relationship between two variables to determine how one may affect the other.

Does not equal causation

Positive Correlation

variables increase & decrease together

Negative correlation

as one variable increases the other decreases

Correlation number facts

The stronger the # the stronger the relationship REGARDLESS of the pos/neg sign

cannot be < or > than 1

stronger relationships = tighter clusters on the graph

Placebo Effect

any observed effect on behavior that is “caused” by the __ ( shows effectiveness of exp. treatment

Double - Blind

Either the participant or the experimenter are aware of which condition people are assigned to ( drug studies )

Single - Blind

Only participants blind - used if experimenter can’t be blind ( gender, age, etc )

Confound

error / flaw in study that is accidentally introduced ( can be called a confounding variable )

Random Assignment

assigns participants to either control or experimental group at random - increase chance of equal representation among groups ( spreads the lefties across both groups ) - allows you to say Cause / Effect

Experiments

purposefully manipulate variables to determine cause / effect

Independent Variable

purposefully altered by researcher to look for effect

Experimental Group

received the treatment ( part of the IV ) that is being tested in an experiment, allowing for comparison with the control group to assess the effects of the independent variable

Control Group

placebo, baseline ( part of the IV ) that does not receive the treatment, used for comparison

Dependent Variable

measured variable ( is DEPENDENT on the independent variable )

Naturalistic observations

Observe people in their natural settings advantage real world validity, disadvantage no cause-and-effect

Case study

Studies, one person ( usually) in great detail advantage collect lots of info disadvantage no cost, and effect

Meta-analysis

Combines multiple studies to increase sample size and examine affect sizes

Descriptive stats

Show shape of the data

Measures of central tendency

• Mean: average (use in normal distribution)

• Median: middle # ( Use in skewed distribution )

• Mode: # that occurs most often

• Bimodal - has two modes - usually indicates good bad scores

Skews

not straight

• Neg skew = mean is to the left (neg side), mode is to the right

• Pos skew = mean is to the right

Measure of variation

• Range - distance between smallest and biggest number

• Standard deviation - average amount the scores are spread from the mean (bigger number is more spread)

Inferential statistics

Establishes significance (meaningfulness)

Statistical significance

Results not due to chance, exp. manipulation caused a difference in mean

Effect size

Quantifies the size of a difference or relationship, providing insight into the practical significance of a finding

Ethical guidelines (IRB approval needed for people)

• Confidentiality - names kept secret

• Informed consent - must agree to be a part of the study

• Informed ascent - minors and their parents must agree

• Debriefing - must be told the true purpose of the study (done after)

• Deception must be warranted

• No harm - mental/physical

Survey

A research method that collects data from participants through questionnaires or interviews to gather information on opinions, behaviors, or characteristics.

• People lying to look better/give off better perception

• Wording effects -how you frame the questions can impact your answers

Random sample (selection)

Method for choosing participants for your study - everyone has a chance to take part, increases generalizability

Representative sample

Sample mimics the general population (ethnicity, gender, age)

Convenience sample

Select participants on availability - less representative and less generalizability this way

Sampling bias

Sampling isn't representative, due to conv. sampling

Cultural norms

Behaviors of a particular group can influence research results

Experimental bias / participant bias

Experimental/participant expectations influences the outcome

Cognitive bias

Bias and thinking/judgment

Confirmation bias

Finding info that supports our pre-existing beliefs

Hindsight bias

Tendency to see events as having been predictable after they have already occurred.

“I knew it all along”

Overconfidence

Overestimate our knowledge/abilities

Hawthorne effect

People change their behavior when watched