PSYCH 211 Test #3

Nominal

category or name; mathematically arbitrary (no math just a response of term, name, or assigned number). Where are you from? SSN? Most independent variables are nominal

Ordinal

rank; order. Relative meaning, but not absolute meaning. 1st, 2nd, 3rd. No mathematical meaning. Rank different fruits. Placement in a race

Interval

Actual amounts, units of measurement. The space between two adjacent scores are equal, but not true zero. Zero can act as a midpoint on a test and not a failure or nothing. Zero does not mean lack of the thing. Excited or calm with neutral as a middle. Rate happiness on a scale of 1-10. What time do you got to sleep?

Ratio

Interval but has an absolute zero point that means lack of something. Can not go negative. Can make ratio statements. How much time do you spend on your phone per week? How many siblings do you have? Independent variables can be this if you were talking about giving dosage of a drug

Sample Size

(N) The number of individuals in the sample

Experiment

used to support causal claims. One variable is manipulated and the other is measured

Manipulated variable

The independent variable. Variables that are being manipulated

Measured variable

The variable that is measured. Dependent variable.

Independent variable

The manipulated variable

Condition/level

an independent variable’s levels

Dependent variable

the measured variable, or outcome variable

Control variable

any variable that an experimenter holds constant on purpose

Comparison group

A group in an experiment whose levels on the independent variable differ from those of the treatment group in some intended and meaningful way. Also called comparison conditions

Control group

a level of an independent variable that is intended to represent “no treatment” or a neutral condition

Treatment group

the other conditions

Placebo group

when the control group is exposed to an inert treatment such as a sugar pill; also known as a placebo control group

Confound

when there are several possible alternative explanations or potential threats to internal validity

Design Confound

an experimenter’s mistake in designing the independent variable; it occurs when a second variable happens to vary systematically along with the intended independent variable

Systematic variability

Differences across the conditions of the IV; due to Individual differences, The effect of our IV, A design confound (aka confounding variable)

Unsystematic variability

Individual differences but not specific to or aligned with IV (occurs both within and across conditions)

selection effect

occur when the method of selecting participants or groups leads to a sample that does not accurately represent the target population

random assignment

randomly assigning participants to groups in a study

Matched groups

separate groups of people but matched on traits that are important

Independent-groups design

between-subjects. Pros → no order effects; lower demand characteristics (participants feel the need to respond differently; good subject effect, expectancy effect)

Cons → need more participants; selection effects: Random assignment doesn’t always work to avoid confounds

Within-groups design

repeated-measures. Pros → needs fewer participants to find a significant effect; Conditions are guaranteed to be equivalent (re: Ps) because you are testing the same person each time

Cons → order effects; higher demand characteristics

Posttest-only design

also known as an equivalent groups, posttest-only design, is one of the simplest independent designs

Randomly assigned to independent variable groups and are tested on the dependent variable once

Pretest/Posttest Design

or equivalent groups, pretest/posttest design, participants are randomly assigned to at least two groups and are tested on the key dependent variable twice—once before and once after exposure to the independent variable

Repeated-measures design

a type of within-groups design in which participants are measured on a dependent variable more than once, after exposure to each level of the independent variable

Order effect

biases caused by the sequence in which participants experience experimental conditions, rather than the treatments themselves

Practice effect

a long sequence might lead participants to get better at the task with some specific order effects due to repeated testing

Fatigue effect

people get tired or bored toward the end with some specific order effects due to repeated testing

carryover effect

some form of contamination carries over from one condition to the next

Counterbalancing

they present the levels of the independent variable to participants in different sequences

Demand characteristics

subtle cues in an experiment that allow participants to guess the researcher's hypothesis, causing them to change their natural behavior to fit that expectation

Descriptive statistics

Used to measure the numbers and compare in one set of data. Not to make inferences outside of the set.

Data matrix

a rectangular, two-dimensional grid used in research to organize information, where rows represent cases or observations and columns represent variables or attributes

Frequency histogram

Normal distribution

scores in the middle are much more frequent

skewed distibution

One pronounced tail, more scores on one side than the other. Not symmetrical

positively skewed

Less at the end loaded at the beginning (Tail points greater)

negatively skewed

Less at the beginning loaded at the end (Tail points less)

Dot plot

a simple data visualization that displays numerical data points as dots on a graph, typically along a single axis (number line) to show frequency, distribution, and outliers

Central Tendency

the peak(s) of the data set.

Mode

score that occurs most frequently. Mostly used with nominal data, but is sometimes used with numerical data in cases where an uncommon amount of one score is present

Exists where the peak is the highest in a normal distribution

Bimodal

two peaks/clusters.

Median

The middle score in a set of data. the middle score when you line up all the scores from lower to highest (has to be numerical [ordinal]).

Used for abnormal distribution. Used with ordinal/interval data

Exists where the peak is highest in a normal distribution

Mean

The average. Add up all scores and divide. Used with ordinal/interval data. Normal distribution is centered around the mean.

Range

From the lowest score to the highest score. Highest score minus lowest score

Interquartile range

Find the median. Find half between the median and the beginning. Find half point from the median and the end. This splits into 4 quartiles. Interquartile range only considers the number in the first quartile and the third quartile as the relevant data. 3Q-1Q= IQR. Distance between third and first quartiles

Variance

Quantify the amount of deviation of the scores compared to the mean. Average of the SQUARED deviations around the mean. SD^2 = (Sigma(X-M)^2)/N. Everything in the parentheses is called the sum of squares. SD^2 is the symbol for variance

Standard deviation

Transforms the variance to avoid certain interpretational problems. Literally just take the variance and throw the square root on the answer. SD=v/SD^2. Gets rid of the issues. Puts it into units of standard deviation making it easier to compare data sets

Deviation score

How much do the scores in the data set deviate from the mean. Take every possible score and subtract the mean. Anyone who scores the mean has a deviation score of zero

box plot

visually summarizes data distribution using a five-number summary: minimum, first quartile (Q1), median, third quartile (Q3), and maximum

outlier

on the tails of the distributions.

Z-Score

z= (x-m)/SD

Relative standing

Compare scores within and across distributions. Is the score to the right or left of the mean? How far away from the mean? Z-score transformation

Correlation coefficient (r)

The degree of association

Equation: r= (Sigma[ZxZy])/N

Scatterplot

plots all values down on a graph to determine association

Linear relation

a statistical association between two variables that forms a straight line when graphed. It signifies that for every unit change in one variable, the other variable changes at a constant, proportional rate

Positive association

x goes up y goes up /

Negative association

x goes up y goes down \

Zero association

occurs when there is no association or correlation between variables

Bar graph

(Histogram) with score/measurement on X and frequency on Y

Line graph