Experimental psychology exam one chapter 3 and 5 study guide

A variable

is anything that can vary be changed or manipulated in type kind or amount

A measured variable

is observed and recorded

A manipulated variable

is controlled

Operationalization

How the construct is measured or manipulated in an actual study

Operationalization example

The researcher will turn a concept or topic they want to research into a research question so if they want to measure coffee consumption then the researcher would either manipulate there coffee consumption or measure how much they drink in a timeframe

Frequency claim –

involves just one variable, describes a single measured variable, Claims that mention the percentage of a variable, the number of people who engage in some activity, or a group’s average on a variable

Association claim –

argues that one level of a variable is likely to be associated with a particular level of another variable. 

Are supported by studies that have at least two measured variables

When two variables are associated with one another, they are correlated

Frequency claim example

“ just 15% of Americans smoke” “ most students don’t know news is fake”

Causal claims

argues that one variable causes a change in another variable (2 variables)

One is manipulated one is measured

Zero correlation

No association between the variables

Positive correlation -

an association in which high goes with high and low goes with low

negative correlations -

high goes with low and low goes with high

Covariance –

The study’s results show that A changes B changes e.g, high levels of A go with high levels of B, and low levels of A go with low levels of B.

Temporal precedence

 the study’s method ensures that a comes first in time before B

Internal validity –

the study’s method ensures that there are no plausible alternative explanations for the change in B A is the only thing that changed no possible alternative outcomes to occur

Know the criteria for establishing causation

covariance, temporal precedence, internal
validity

The third variable problem –

there is a third variable impacting the two variables that are correlated

Construct validity –

How well the variables in the study are measured or manipulated. The extent to which the operational variables in a study are a good approximation of the conceptual variables (how well you have operationally defined your variables. do your tools accurately represent the construct you are attempting to measure).

External validity –

The extent to which the results of a study generalize to some larger population ( whether the results from this sample of teenagers apply to all U. S. teens) as well as to other times or situations (whether the results based on coffee apply to other typed of caffeine) generalizability

Statistical validity –

How well the numbers support the claim that is how strong the effect is and the precision of the estimate (the confidence interval). Also takes into account whether the study has been replicated

Internal validity -

In a relationship between variable A and another B, the extent to which A, rather than some other variable C, is responsible for changes in B

confidence interval (CI)

A given range indicated by a lower and upper value that is designed to capture the population value for some point estimate (e.g., percentage, difference, or correlation); a high proportion of CIs will capture the true population value.

margin of error of the estimate

In the context of a percentage estimate, an inferential statistic provides a range of values that has a high probability of containing the true population value.

Lack of random assignment is a threat to what type of validity?

Internal validity

Self-report measure –

operationally defining a variable by recording a participant’s responses to questions about themselves (questionnaire, interview, written or online format) most common way to operationally define a variable

Observational measure –

(behavioral measures) Operationally defining a variable by recording observable behaviors (we use behaviors to infer constructs)

Physiological measure –

Operationally define a variable by recording biological data (brain activity, heart rate, respiration, blood- allows you to check levels of different things such as insulin medication and cortisol levels)

Nominal –

categorical in groups with no rank order

Ordinal -

when the numerals of a quantitative variable represent a ranked order.

Interval -

requires two conditions: First, unlike ordinal scales, the numerals represent equal intervals (distances) between levels, and second, there is no “true zero”

Ratio -

when the numerals of a quantitative variable have equal intervals and when the value of 0 truly means “none” of the variable being measured.

Test-retest reliability –

Same test two times with the same or similar results

Interrater reliability –

The instrument used to gather the data is a human  (typically, we give them a guild line or coding system that they use to record the behavior of who they are observing so you compare their rating to make sure they are observing the same thing) (same participant at the same time)

Internal reliability –

In a measure that contains several items, the consistency in a pattern of answers, no matter how a question is phrased. Also called internal consistency. Looking at the internal consistency of the tool/instrument/test. (The passage of time can impact performance.) All odd and even questions is a better way to test consistency the first and second half as it spreads out the tiredness questions and takes it into account where the first and second half, there is a passage of time as a variable.,

Criterion validity-

An empirical form of measurement validity that establishes the extent to which a measure is associated with a behavioral outcome with which it should be associated. (you want a strong positive correlation)

Convergent validity –

An empirical test of the extent to which a self-report measure correlates with other measures of a theoretically similar construct

Discriminant validity-

An empirical test of the extent to which a self-report measure does not correlate strongly with measures of theoretically dissimilar constructs. Also called divergent validity. Your self-report measure is less strongly associated with self-report measures of dissimilar constructs (opposite of convergent)

Face validity –

It looks like what you want to measure

Content validity-

The measure contains all the parts that your theory says It should contain

Know how we use correlation to evaluate reliability

you can use a positive correlation coefficient to show consistency over time with a test-retest reliability.

Know how we use correlation to evaluate validity

to show criterion validity correlation coefficients and scatterplots can be used

conceptual definition -

is the definition of the variable in question at a theoretical level