Quantitative research -  research that details number-based results; objective 

• It is or is not statistically significant

Qualitative research - research that details non-number based research: qualities, characteristics, texts

• Used for correlational studies

Theory - a strongly supported hypothesis based on research that predicts interrelated events

Hypothesis - a testable question that is typically based on a theory; can be proven or disproven

Falsifiable - the quality of a hypothesis that allows it to be proven false based on an experiment or research results

Operational definition - a description of a variable that is objective and allows for inter-rater reliability

Replication - the process of repeating the original experiment to bolster or verify previously concluded results

Quasi experiment - an experiment in hi h the independent variable cannot truly be controlled 

Meta-analysis - combining the results of multiple experiments that explain separate phenomena into a single theory 

Occam’s Razor - principle that states if there are two equally legitimate scientific explanation for a phenomenon, you should always pick the simpler one 

Extraordinary Claims - any extraordinary claim requires extraordinary evidence 

Falsifiability - for an experiment to be legitimate, it must be able to be disproven

Case study - an in-depth study of a particular entity that allows a researcher to gather complex information but does not  allow for generalization

Naturalistic Observation - The observation of non-controlled environments  

• Falls victim to Hawthorne effect - people change their behavior as they known that they are being observed 

Survey - a group of individuals chosen for questionnaires or interviews regarding their behaviors, opinions, etc. to use for generalizations 

• Falls victim to desirability bias - people over-report good qualities and underreport bas qualities 

Longitudinal study - the same group of people are tested repeatedly over an extended period of time to observe changes 

• Falls victim to attrition - loss of individuals over time

Cross Sectional study - gathering data from differently-aged people at the same time 

• Falls victim to cohort effect - differences between generations based on societal or cultural experiences 

Sampling bias -  skewed result of a survey due to the individuals/choices selected that can threaten the internal validity of a study and prevent data from being accurately generalized 

Desirability bias - the tendency to overreport desirable attributes and underreport undesirable attributes

Hindsight bias - the tendency for people to perceive past events as more predictable than they were

Experimenter bias/self-fulfilling prophecy - when a researcher’s/person’s cognitive biases causes them to influence their subjects/a situations outcome

Intentional bias - purposefully altering data to change the results of a study

Overconfidence - the tendency to overestimate one's knowledge or abilities in a certain area that leads to complications 

Hawthorne Effect - when participants’ behavior changes as a result of being observed, rather than as a result of intervention  

Confirmation bias - the tendency to seek out evidence that supports our beliefs and deny, dismiss, or distort evidence that contradicts it  

Sampling - when researchers select a group to study

• Sample - the subset of participants selected from a population

  • Random sampling - every individual in the population has an equal chance of being selected (representative sample)

  • Representative sample - a sample that reflects the characteristics of an entire population

  • Convenience sampling - researcher selected participants who are available; can be biased or unrepresentative

  • Stratified sampling - purposefully selecting subjects from subsets of a population (eg. one of each represented) 

Assignment - how researchers decide which group participants will be a part of 

Descriptive Statistics - organize data meaningfully

Inferential Statistics - apply results from a sample to a population

• Representative sample leads to little inconsistency

• P-values lesson than 0.05 are statistically significant, prove that the variable led to the results (cause and effect)

Frequency distribution - show how often sets of data occur

Central tendency - snapshot/summary of data

• Mean - average of data

• Median - number in middle (half above half below) **best to use for accuracy

• Mode - most occurring set of data

  • ***outliers can affect all of these

• Range - subtracting the highest value by the lowest value

Standard Deviation - how much scores vary around the mean score 

• Typically fall into a bell curve with more scores near the mean and fewer at the extremes

• Smaller deviations mean that the data is closer to the mean

Correlations - the relationship between variables; help PREDICT but cannot show causation

• Illusionary - belief that two variables are related when they are not

• Positive - variables covary in the same direction

• Negative - variables covary in opposite directions

  • A correlation coefficient closer to +/- 1 is a stronger correlation while closer to 0 is weak/little correlation