Chapter 2: Psychological Methods Flashcards by Felisa Pador

What should you consider when studying humans?

Complexity, Variability, Reactivity

How well did you know this?

Not at all

Perfectly

Humans have 500 neurons in the brain (consideration).

Complexity

How well did you know this?

Not at all

Perfectly

Every person is different (consideration).

Variability

How well did you know this?

Not at all

Perfectly

Reactions differ when observed vs. not observed (consideration).

Reactivity

How well did you know this?

Not at all

Perfectly

A stand-in for humans that will be simpler (eg. flatworms that have 500 neurons or lab rats)

Model Species

How well did you know this?

Not at all

Perfectly

Other sources for people’s beliefs (limited):

Experience (Subjective), Intuition (Common Sense), An Authority (Lectures)

How well did you know this?

Not at all

Perfectly

Involves using evidence from the senses as the bases for conclusions (empirical method/research).

Empiricism

How well did you know this?

Not at all

Perfectly

The Scientific Process Steps

Identify a question
Form a hypothesis & Gather information
Test hypothesis by conducting research
Analyze data
Build a body of knowledge

— If the data you collected support the hypothesis, you experiment supported your hypothesis. It is supporting your theory. If not, revise theory and come up with new hypothesis.

How well did you know this?

Not at all

Perfectly

A tentative explanation or prediction about a phenomenon (given a theory, I would expect this outcome).

Hypothesis

How well did you know this?

Not at all

Perfectly

A set of formal statements that explain how and why certain events are related to one another (big picture).

Theory

“Attachment Theory”
— how humans attach throughout their lives

How well did you know this?

Not at all

Perfectly

After viewing a behaviour, propose an explanation that makes sense in the context.

eg. A person lied down in the bench because they are tired.

Hindsight Understanding

How well did you know this?

Not at all

Perfectly

Test possible explanations through scientific method.

eg. To find out why a person lied down on the bench, you’d do an experiment or survey.

Hypothesis-testing

How well did you know this?

Not at all

Perfectly

What makes a good theory?

— Organized Information
— Testable (falsifiable)
— Predictions are supported by research
— Law of Parsimony

How well did you know this?

Not at all

Perfectly

The idea that all things being equal, the simplest explanation is the best.

Law of Parsimony

aka Occam’s Razor

How well did you know this?

Not at all

Perfectly

How do we get our data?

Obtain information related to hypothesis.

How well did you know this?

Not at all

Perfectly

Precise, repeatable methods of measurement.

Scientific Observation

How well did you know this?

Not at all

Perfectly

Standing outside your building and realizing it’s cold outside.

Casual Observation

How well did you know this?

Not at all

Perfectly

Any characteristic that can vary (measurable).

Variable

How well did you know this?

Not at all

Perfectly

Define a variable in terms of specific procedures used to produce or measure it.

Concrete and measurable terms.

Operational Definition

How well did you know this?

Not at all

Perfectly

Always produce the same score when measuring the same thing (good measurement).

Reliability

How well did you know this?

Not at all

Perfectly

Must be conceptually related to the property of study (good measurement).

Validity

How well did you know this?

Not at all

Perfectly

Ability of a measure to detect the conditions specified in the operational definition (good measurement).

“powerful” way to measure

Power

How well did you know this?

Not at all

Perfectly

Participants report on their own knowledge, beliefs, feelings or behaviour (types of measures).

eg. questionnaire or interview

Self-report

A downside in using self-report. Desire to make good impression.

Social Desirability Bias

— don’t ask leading questions
— include questions for dishonesty
— ask someone else in their life

Observers record observable behaviour (types of measures). Requires operational definition. Observers must be trained to consistently code their observations.

Observational

Issues with overt behaviour:

Demand Characteristics — observational setting that make people behave as they think that they should — participants can change their behaviour when observed

Make use of pre-existing records or documents (types of measures).

Archival Measures | — limited to records that already exist

Specialized tests designed by psychologists to measure particular variables (types of measures).

Psychological Tests

Examples of Psychological Tests:

Personality Tests — measure personality traits Intelligence Tests — measure performance on intelligence testing tasks Neuropsychological Tests — help diagnose abnormal brain function

Seek to explain how we behave especially in natural environments (types of research). eg. case studies, naturalistic observation, surveys

Descriptive Research — all variables measured — natural contexts — extraneous factors not controlled

Issues with Case Studies (type of descriptive research):

— poor method of determining cause-effect relationships — generalizability is questionable — researcher bias (if you are only measuring one person, all measurements are bias)

Observing people in their natural environment (type of descriptive research): eg. Jane Goodall observing chimpanzees in the wild

Naturalistic Observation

Advantages of Naturalistic Observations:

— rich description of behaviour | — can avoid demand characteristics

Limits of Naturalistic Observations

— ethical problems — long periods to get single measure — difficult to study particular behaviours

The entire set of individuals about whom we wish to draw a conclusion. eg. university students

Population

A subset of individuals drawn from a population. eg. 1000 students from each university

Sample

A sample that reflects the important characteristics of the population. eg. It should include all characteristics majors, genders, races, etc.

Representative Sample

Method of sampling where every member of population has an equal probability of being chosen to participate. eg. random number generator choosing students across university

Random Sampling — not always possible but it's okay if it doesn't matter, multiple experiments conducted on different samples have similar results, similarity of sample and pop'n is reasonable

Describing the center of the data.

Central Tendency

Average value of all measurements.

Mean

Value of middle of the distribution.

Median

Value of the most frequently observed measurement.

Mode

Normal Distribution

— aka Gaussian distribution, bell-curve | — symmetrical, central peak, trails off to both sides

How much our measurements differ from one another:

Variability

Value of largest measurement in a frequency minus the smallest.

Range

Describes the average difference between the measurements in a frequency distribution and the mean of the distribution.

Standard Deviation

Positively Skewed

Mode > Median > Mean

Negatively Skewed

Mean > Median > Mode

Box Plots

— box covers two quartiles of data — box is 50% of data (interquartile) — line inside shows the median

Looking for an association between two (or more) measured variables (types of research).

Correlational Research

"Does X cause Y or does Y cause X?"

Bidirectionally Problem

X might be causing Y but maybe there's a third variable that is causing X and Y— variable Z.

Third Variable Problem

Describe relationship between variables.

Correlation Coefficient (r) — ranges from -1.0 to 1.0 — signs indicate direction — absolute value indicate strength

Advantages of Correlational Research:

— show strength of relationship — can be used to make predictions using data you have; extrapolate — identify real-world associations

Disadvantages of Correlational Research:

— can't assume cause effect relationship — association not cause — relationship may be from unmeasured variable