• Shuffle
Toggle On
Toggle Off
• Alphabetize
Toggle On
Toggle Off
• Front First
Toggle On
Toggle Off
• Both Sides
Toggle On
Toggle Off
Toggle On
Toggle Off
Front

### How to study your flashcards.

Right/Left arrow keys: Navigate between flashcards.right arrow keyleft arrow key

Up/Down arrow keys: Flip the card between the front and back.down keyup key

H key: Show hint (3rd side).h key

A key: Read text to speech.a key

Play button

Play button

Progress

1/51

Click to flip

### 51 Cards in this Set

• Front
• Back
 Statistics refer to numbers that describe a sample Descriptive Statistics organize data from a sample by showing it in a meaningful way. You cannot draw conclusions from data beyond the sample. Examples are percentiles, frequency distributions, graphs, measures of central tendency, an variability. Parameters refer to numbers that describe populations Nominal Variables A type of frequency distribution. No order or relationship among the variables other than to separate them into groups. ex: male, female, repub, democ. rejecting the null hypothesis null hypothesis says no relationship exists Ordinal Variables type of frequency distribution. Variables are arranged b order. Alpha level of significance <.05 or <.01 Interval Variables type of frequency distribution. Can show order AND spacing because equal spacing lie between the values. There is no real zero. Ex: temperature Type 1 errors incorrectly rejecting the null hypothesis Ratio Variables type of frequency distribution. Have order, equal intervals, and a real zero. Ex: Age Type 2 errors wrongly accept null hypothesis Mean Average of a set. Standard error of the mean calculates how "off" the mean might be in either direction t-tests compare means of 2 groups Median Value that lies in the center of a number set organized in ascending order. If there's an even number of values, take the average of the two middle values. chi-square tests n-cases in a sample. Will tell us whether groups are significantly different in size Mode the most frequently occurring value in a set ANOVA (Analysis of variance) flexible. like a t-test in that it analyzes differences among means. can do more than 2 groups Variance or Standard Deviation tells us how much variation there is among n number of scores in a distribution. Variance = (((Score 1-Mean)^2+...(Score n-Mean)^2)/n)^1/2 One-way ANOVA tests whether the means on one outcome or dependent variable are sig different across groups The Normal Distribution bell curve. It's unimodal (one hump) and majority of scores fall in the middle range. Two-way ANOVA test the effects of two independent variables or treatment conditions at once Z-scores on a normal distribution refer to haow many standard deviations a score is from the mean (range from -3 to 3) factorial analysis of variance used when an experiment involves more than one independent variable. This analysis can separate the effects of diffeent levels of different variables. Can isolate main effects and can identify interaction effects. You can combine the independent variables Standard normal distributions normal distributions can be standardized so that one can compare tests with different standard deviations. Standardizing makes the mean 0 and the standard deviation 1. Analysis of Covariance (ANCOVA) tests wheteher at least two groups covary. Can adjust for pre existing differences between groups. z-scores and percentile ranks on the normal distribution 68% scores live within one standard deviation of the mean. from -3 to -2 = 2% -2 to -1 = 14% -1 to 0 = 34% 0 to 1 = 34% 1 to 2 = 14% from 2 to 3 = 2% Criterion referenced tests measure mastery in a particular area positively skewed distribution hump skewed to the left Domain referenced tests measure less defined properties (like intelligence) and need to be checked for reliability and validity. Negatively skewed distribution hump skewed to the right Bimodal distribution two humps Reliability How stable a measure is Platykuric distribution Flat equal frequency across all values. Test-retest reliability measured by the same individual taking the same test more than once. on a test with high test-retest reliability that person would get approx the same score each time. Correlations show relationships NOT CAUSALITY between variables. Split-half reliability measured by comparing an individuals performance on two halves of ht esame test. This reveals the internal consistency of a test. Can also increase internal consistency by item analysis (analyzing how a large group responded to each measure item) Positive Correlation As one variable increases so does the other one Validity how well the test measures a construct Negative Correlation As one variable increases the other variable decreases Internal validity measures the extent to which the different items within a measure "hang together" and test the same thing Curvilinear correlation variable relationship looks like a curved line. Example: Arousal and performance. Low arousal and high arousal lead to poor performance, but a medium amt of arousal leads to successful performance. External Validity the extenet to which a test measures what it intends to measure. 4 aspects are concurrent validity, constuct validity, content validity, face validity. Zero correlation no relationship Concurrent validity whether scores on a new measure positively correlate with other measures known to test the same construct. Process is cross validation. Pearson r correlation coefficient expresses correlation. r ranges from -1 to 1. -1 indicates perfect negative correlation, 1 indicates perfect positive correlation, 0 indicates no relationship. Strength of relationship measured by how far away it is from zero. Construct Validity whether the test really taps the abstract concept being measured Spearman r correlation coefficient correlation used when the data is in the form of ranks. Detemines the line that describes a linear relationship content validity whether the content of the test covers a good sample of the construct being measured Regression the step beyond correlations. A statistical regression allows you to identify a relationship between two variqables and make predictions about one variable based on another variable. face validity whether the test items simply look like they measure the construct Campbell and Fiske created the multitraitmultimethod technique to determine th validity of tests