Lecture quiz 1 Janowsky

Sign if below

.05->or Lowe (.000)

the mean of distribution of z score is always

0 This is so because when you change each raw score to a Z score, you take the raw score minus the mean. So the mean is subtracted out of all the raw scores, making the overall mean come out to 0 Which is why when you add all z scores they come out to 0.

strength of correlation: trivial

0.1 and below (also negatives)

strength of correlation: large/strong

0.5 and above (also negatives)

the standard deviation of z score is always

1 This is because when you change each raw score to a Z score, you divide the score by one standard deviation.

Split-half reliability

A measure of reliability in which a test is split into two parts and an individual's scores on both halves are compared.

population

ENTIRE group of people to which a researcher intends the results applies to

Postive correlation

If you multiply a high Z score by a high Z score, you will always get a positive cross-product. —if you multiply a low Z score by a low Z score, you also always get a positive cross-product. This is because no matter what the variable, scores below the mean are negative Z scores, and a nega-tive multiplied by a negative gives a positive. If highs on one variable go with highs on the other, and lows on the one go with lows on the other, the cross-products of Z scores always will be positive.

Test-restest Reliability

Is determined by comparing scores earned by the same person on the same test taken at different times.

Mean

Most commonly used measure of central tendency Usually the best measure of central tendency is the ordinary average, the sum of all the scores divided by the number of scores. Used with Equal interval variables.

negative correlation

On the other hand, with a negative correlation, highs go with lows and lows with highs. In terms of Z scores, this would mean positives with negatives and negatives with positives. Multiplied out, that gives all negative cross-products. If you add all these negative cross-products together, you get a large negative number.

descriptive statistics

Psychologists use descriptive statistics to summarize and describe a group of numbers from a research study. ( What we've been learning so far) Describes the sample you collected

inferential statistics

Psychologists use inferential statistics to draw conclu-sions and to make inferences that are based on the numbers from a research study but that go beyond the numbers. For example, inferential statistics allow researchers to make inferences about a large group of individuals based on a research study in which a much smaller number of individuals took part Makes inferences about the population based on your sample

Correlation in Research Articles

Scatter diagrams are occasionally included in research articles, most commonly when there are relatively small numbers of individuals involved, such as in many perception and neuroscience studies. But sometimes they are included in larger studies, Correlation coefficients are very commonly reported in research articles. positive correlation (r=.51). (r(n-2)=.92, p<.05 Tables of correlations are common when several variables are involved.

validity

The ability of a test to measure what it is intended to measure

standard deviation

The most widely used number to describe the spread of a group of scores. how much the data varies from the mean (square root of the variance) Approx the average amount that scores in a distribution vary from the mean.

central tendency

Three measures: mean, median, mode of a group of scores (a distribution) refers to the middle of the group of scores. Typical or most representative value of a group of scores.

standard error of estimate

What you use to calculate confidence intervals

Logic of Figuring the Linear Correlation

Z scores are so useful when figuring the exact correlation. if you multiply a score on one variable by a score on the other variable, which is called a cross-product

bimodal dis-tribution.

a distribution has two fairly equal high points,

equal-interval variable (level) ( numeric variable) {table 1-2}

a variable in which the numbers stand for approximately equal amounts of what is being measured. For example, grade point average (GPA) is a roughly equal-interval variable , since the difference b e t w e e n a GPA of 2.5 and 2.8 means about as much as the difference between a GPA of 3.0 and 3.3 (each is a difference of 0.3 of a GPA).

criterion variable

a variable that is predicted in formulas the predictor variable is usually labeled X, and the criterion variable is usually labeled Y.

Discriminant Validity

an empirical test of the extent to which a measure does not associate strongly with measures of other, theoretically different constructs ( loneliness and weight)

Convergent validity

an empirical test of the extent to which a measure is associated with other measures of a theoretically similar construct (Both test for loneliness, related)

ratio scale ( equal-interval variable) {table 1-2}

an equal-interval variable is measured on a ratio scale if it has an absolute zero point, meaning that the value of zero on the variable indicates a complete absence of the variable. For example, the number of siblings a person has is measured on a ratio scale, because a zero value means having no siblings. we can say that a person with four siblings has twice as many siblings as a person with two siblings.

why are normal curves natural?

because everything is random and the results spread each other out

strength of correlation: small/weak

between 0.1 and 0.3 (also negatives)

strength of correlation: moderate

between 0.3 and 0.5 (also negatives)

standard deviation and variance are _____ estimators

biased

statistics

branch of mathematics that focuses on the organization, analysis, and interpretation of a group of numbers.

ruling out possible directions of causality

by conducting a true expierement true experiment, participants are randomly assigned to a particular level of a variable and then mea-sured on another variable. ex: example of this is the study in which participants were randomly assigned (say, by flipping a coin) to different numbers of exposures to a list of words, and then the number of words they could remember was measured. There was a .82 correlation between number of exposures and number of words recalled. In this situation, any causality has to be from the variable that was manipu-lated (number of exposures) to the variable that is measured (words recalled).

variable

characteristic that can have different values ex: level of stress, Height is a variable, social class is a variable,

haphazard sampling

choosing whoever is available or happens to be first on the list

correlation matrix

common way of reporting the correlation coefficients among several variables in a research article; table in which the variables are named on the top and along the side and the correlations among them are all shown

strongest way to to rule out possible directions of causality

conduct true experiments

correlation APA

correlation APA r= correlation (r(n-2)= ._ _ , p < .05 or n.s. - (italicize r, p, and n.s.)

Interpreting the Correlation Coefficient

correlation coefficient describes the linear relationship between two variables. result of dividing the sum of the products of Z scores by the number of people in the study is called the correlation coefficient. (+or -) of a correlation coefficient tells you the direction of the lin-ear correlation between two variables (a positive correlation or a negative correla-tion). correlation coefficient (r) measure of degree of linear correlation between two variables ranging from -1 (a perfect negative linear correlation) through 0 (no correlation) to +1 (a perfect positive correlation) correlation coefficient is a descriptive statistic,

reliability

degree of consistency or stability of measure across repeated measures Ability of a test to yield very similar scores for the same individual over repeated testings

2 branches of statistics

descriptive and inferential

sample statistics.

descriptive statistics, such as the mean or standard deviation, figured from the scores in a group of people studied the mean, variance & SD of the scores in a sample use ROMAN letters

APA

descriptives (M=, SD=) in ITALICS "p" and "r" are ALSO italicized NO 0 before decimals for correlation or significance

skewed distribution

distribution in which the scores pile up on one side of the middle and are spread out on the other side; distribution that is not symmetrical. Has a tail. The side with the fewer scores (the side that looks like a tail) is considered the direction of the skew. If tail is to left it is left skew

multimodal distribution

distribution with two or more high points

The Normal Curve and the Percentage of Scores Between the Mean and 1 and 2 Standard Deviations from the Mean

exactly 50% of the scores in a normal curve are below the mean, because in any symmetrical distribution half the scores are below the mean approxi-mately 34% of the scores are always between the mean and 1 standard deviation from the mean. MORE SCORES HERE Normal curve with approximate percentages of scores between the mean and 1 and 2 standard deviations above and below the mean

linear prediction rule

formula for making predictions; that is, formula for predicting a person's score on a criterion variable based on the person's score on one or more predictor variables. ex: predicting someones gpa based on SAT

symmetrical distribution

frequency distribution in which all values have approximately the same frequency. (if you fold the graph of a symmetrical distribution in half, the two halves look the same)

Theory

is a general principle or set of principles about a class of events used to explain an important psychological process. Used to explain phenomena, predict new information, allow scientist to use logical deductive reasoning to formulate testable hypothesis

hypoth-esis

is a prediction intended to be tested in a research study.

rank-order variable aka ordinal (level) ( numeric variable) {table 1-2}

is a variable in which the numbers stand only for relative ranking. A student's standing in his or her graduating class is an example. The amount of difference in underlying GPA between being second and third in class standing could be very unlike the amount of difference between being eighth and ninth. doesn't tell you the exact difference in amount of what is being measured

z score

is the number of standard deviations the actual score is above or below the mean. If the actual score is above the mean, the Z score is positive. If the actual score is below the mean, the Z score is negative. Used mean and SD to find z score. example, Jerome has a score of 5, which is 1.60 units above the mean of 3.40. One standard deviation is 1.47 units; so Jerome's score is a little more than 1 sd above the mean

inductive reasoning

look for a trend or pattern then generalize it (specific->general)

types of causality and three directions of causality

means: what is causing what x causes y, y causes x, and 3rd possibility causing both ex: activités and martial satisfaction 1. doing activities together can make the couple more satisfied 2. People who are more satisfied choose more exciting activities 3. Or some other variable like having less stress at work makes more people happy in their marriage and gives them more time and energy to do activities

random selec-tion (sampling)

method for select-ing a sample that uses truly random procedures (usually meaning that each person in the population has an equal chance of being selected);

Median

middle score when all the scores in the distribution are in order. Sometimes median is better than the mean when there are extreme numbers that affect the mean but not the median.

central tendency in research articles

mode, median and variance are rarely reported in articles Mean and SD are common in articles tables and text. Page 56

Mode

most common single value in a distribution. In a perfectly symmetrical unimodal distribution, the mode is the same as the mean. When mean and mode are not the same the mode is not a good a good way to describe central tendency, bc does not reflect many aspects of distribution. Usual way for describing central tendency for a nominal variable: like how is part of the most religions.Used in nominal variables. Used when distribution has one or more outlier, rank-order.

left skew

negatively skewed

zero correlation

no association

n.s

non significant

variance

one kind of number that tells you how spread out the scores are, around the mean. the variance is the average of each score squared difference from the mean. The more spread out a distribution is the larger the variance bc being being spread out makes the deviation scores bigger.

raw score

ordinary score (or any number in a distribution before it has been made into a Z score or otherwise transformed). z=x-m/SD the x is the raw score

Unreliability of Measurement

our measures are not perfectly accurate or or reliable. eX: Asking someone hours of sleep on a day three weeks ago and their mood the next day, It just wouldn't be accurate. There answer is prob not close to how many hours they slept. the true correlation between sleep and mood could be high, but the correlation in the particular study might be quite low, just because there is lots of "random noise" (random inaccuracy) in the scores Consider another example. Height and social power have been found in many studies to have a moderate degree of correlation. However, if someone were to do this study and measure each person's height using an elastic measur-ing tape, the correlation would be much lower.

score

particular person's value on a variable. in any variable, each person studied has a particular number or score that is his or her value on the variable. ex: your score on the stress variable might have a value of 6. Another student's score might have a value of 8.

illusory correlation

perceptions of relationships that do not really exist ex: level of happiness and amount of red lights on the way to school

right skew

positively skewed

values

possible number or category that a score can have. ex: level of stress values from 0 to 10, just a number, such as 4, -81, or 367.12. A value can also be a cat-egory, such as male or female, or a psychiatric diagnosis—major depression, post-traumatic stress disorder.

Psychology is empirical ( unbiased observations)

public (published), replicable (repeatable, you can get same findings), and creates theories.

numeric variable. { table 1-2}

quantitative variables. 2 levels of measurement stress ratings example: the scores are numbers that tell you how much there is of what is being measured. In the stress ratings example, the higher the number is, the more stress there is.

two types of sampling

random sampling and haphazard sampling

Prediction in Research Articles

rare for bivariate linear prediction rules to be reported in psychology research articles; usually, simple correlations are reported. Sometimes, however, you will see regression lines from bivariate predictions. This is usually done when there is more than one group and the researcher wants to illustrate the difference in the linear pre-diction rule between the two groups. Multiple regression results are common in research articles and are often reported in tables Usually, the table lays out the regression coefficient for each predictor variable, the table may also give the correlation coefficient (r) for each predictor variable with the criterion variable.

attenu-ation.

reduction in a correlation due to unreliability of measures. ex: sleep study, height and social power the correlations reported in that study usually underestimate the true correlation between the variables (the correlation that would be found if there was perfect measurement)

normal distribution

requency distri-bution that follows a normal curve.

sample

scores of a PARTICULAR group being studied. Use this group to generalize for population.

unimodal distribution.

shape has only one main high point: one high "tower" in the histogram. in the stress ratings study, the most frequent value is 7, giving a graph only one very high area.

regression line

shows the relation between values of the predictor variable and the predicted values of the criterion variable. visualize a linear prediction rule as a line on a graph in which the horizontal axis is for values of the predictor variable (X) and the vertical axis is for predicted scores for the criterion variable 1Yn

Scatter diagram

shows the relationship between two variables Values of one variable are on the horizontal and other variable on the vertical axis. Postive linear correlation:dots go up to the right negative linear correlation: dots go down to the right.

restriction in range

situation in which you figure a correlation but only a limited range of possible values on one of the variables is included ;don't have full data ex: testing an entire range of grade levels on geography knowledge ( larger positive correlation ) vs only first three grades than u would see a much smaller correlation and researcher would be making a mistake by concluding that grade level is only slightly related to knowledge of geography in overall grades. = LIMITED RANGE OF GRADE LEVELS

no linear correlation.

some people highs on one variable would go with highs on the other variable (and some lows would go with lows), making positive cross-products. highs on one variable would go with lows on the other variable (and some lows would go with highs), making negative cross-products. Adding up these cross-products for all the people in the study would result in the positive cross-products and the negative cross-products cancelling each other out, giving a result of 0

normal curve

specifically and mathematically defined bell shaped frequency. distribution that is symmetrical and unimodal, approximate a precise and important mathematical distribution called the normal distribution ( normal curve). Researchers compare the actual distributions of the variables they are studying to the normal curve to check if it at least follows a normal curve.

slope

steepness of the angle of a regression line in a graph of the relation of scores on a predictor variable and predicted scores on a criterion variable; number of units the line goes up for every unit it goes across.

deductive reasoning

taking general facts and eliminating false facts (general->specific)

correlation

the association between scores

error

the difference between a person's predicted score on the criterion variable and the persons ACTUAL score on the criterion variable ( the weaker the correlation the further apart the scores will be)

population parameters.

the mean, variance, & SD of a population use GREEK letters

intercept

the point where the regression line crosses the vertical axis the intercept is the predicted score on the criterion variable (Y) when the score on the predictor variable (X) is 0.

attenuation

the reduction in a correlation due to unreliability of measures (bad research/experimental procedures)

false consensus effect

the tendency to overestimate the extent to which others share our beliefs and behaviors ( movie theatre example)

sample mean is an _______ estimator

unbiased

negative correlation

up, down or down, up

positive correlation

up, up or down, down

diving by n vs n-1

use n-1 ( ss/n-1)(SPSS will divide by n-1) when you have scores from a particular group of people ( sample) and want to estimate what the variance is for the larger group of people who these individuals represent. Infrential stats ss/n using descriptive statistics when describing the variation in a particular group.

continuous variable

variable for which, in theory, there are an infinite number of values between any two values. ex: weight, temp etc

discrete variable

variable that has specific values and that cannot have values between these specific values. ex: siblings

predictor variable

variable that is USED to predict scores on another variable in formulas the predictor variable is usually labeled X, and the criterion variable is usually labeled Y.

nominal variable aka categorical variables (level){table 1-2} ( discrete variables)

variable with values that are categories (that is, they are names rather than numbers). Also called categorical variable. nominal variable gender, the values are female and male.