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Exam Review Guide Psych 215

by: Stephanie Bahr

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6

Exam Review Guide Psych 215 Psych 215

Stephanie Bahr
UWW
BASIC STATISTICAL METHODS
Shen Zhang

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COURSE
BASIC STATISTICAL METHODS
PROF.
Shen Zhang
TYPE
Study Guide
PAGES
6
WORDS
KARMA
50 ?

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This 6 page Study Guide was uploaded by Stephanie Bahr on Monday September 28, 2015. The Study Guide belongs to Psych 215 at University of Wisconsin - Whitewater taught by Shen Zhang in Fall 2015. Since its upload, it has received 106 views. For similar materials see BASIC STATISTICAL METHODS in Psychlogy at University of Wisconsin - Whitewater.

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Date Created: 09/28/15
EXAM REVIEW Empirical 0 Based on observationsbased on the 5 senses o if it isn t observable then it s not scienti c o MUST BE BASED ON OBSERVATIONS 0 data is usually collected rst hand but can be second hand Population vs Sample Population 0 group of individuals who share the same characteristics Sample 0 a select few of the population Why use a sample Because it s very dif cult to test an entire population Sampling public opinion is like sampling soup one spoonful can re ect the taste of the whole pot if the soup is wellstirred George Gallup Random Sample 0 a subset of the population 0 everyone in the population has an equal chance to be selected as part of the random sample Experiment 0 a scienti c procedure undertaken to make a discovery test a hypothesis or demonstrate a known fact 0 in order for it to be an experiment the experimenter must have total control over over the experiment Independent Variable 0 variable the experimenter manipulates Dependent Variable 0 variable that changes based on the independent variable 0 is dependent on the independent variable Ex When testing if violence in media effects aggression in children the independent variable is the violence in the media and the dependent variable is the aggression level of the children after watching violent programs Random Assignment 0 an experimental technique for assigning human participants or animal subjects to different groups in an experiment e g a treatment group versus a control group using randomization such as by a chance procedure e g ipping a coin or a random number generator 0 this creates equivalent groups 0 gets rid of systematic biases that individuals may have QuasiExperiment 0 just like a regular experiment 0 the only difference is at least one independent variable is not manipulated O comparing different populations 0 eX are alcoholics more impulsive than nonalcoholics I can t use random assignment because the people in each group needs to be either all alcoholics or all nonalcoholics Describe vs Inference Descriptive Statistic O a single number to describe data from a sample 0 measurable characteristics of a sample Organize summarize and simplify 0 Provide basic information 0 EX mean mode median 0 more eX standard deviation Inferential Statistic O draW inferences about characteristics of a population based on the sample Two Broad Goals of Inferential Statistics 0 estimate the value of population parameters 0 hypothesis testing Types of Variables O scales of measurement 0 quantitative vs qualitative O discrete vs continuous Scales of Measurement 0 Nominal O categorical data 0 no numbers just category I eX sorting people into different ethnicities O Ordinal 0 data that can be placed in a speci c top down or bottom up order I eX college ranking I racing 1st 2nd 3rd 0 Interval 0 includes characteristics of ordinal data and quali es information with equal intervals I eX Celsius amp Fahrenheit 0 Ratio 0 includes characteristics of interval data I true zero point 0 eX weight height speed Chance Difference 0 equivalent groups will always differ in some unpredictable way 0 this is because we draw from the sample and not the population Chance 0 random 0 beyond what we predict 0 not systematic Correlation and Regression 0 whether two or more variables change together Subject Variable o characteristic or attribute of subject that can be measured but not manipulated by researcher CORRELATION DOES NOT EQUAL CAUSATION 0 just because two or more variables are changing together doesn t mean that one variable changing is the cause of the other variable changing Correlation Coeffcient o a number between 1 and 1 calculated so as to represent the linear dependence of two variables or sets of data Regression Analysis 0 estimating the relationships among variables 0 use one to predict the other Frequency Distribution and Graphs Ungrouped frequency distribution STEPS 0 Listing all possible score values from the lowest to the highest 0 Placing a tally mark beside a score each time it occurs 0 Simple frequency of occurrence of each scoref o a count of the number of times each score occurs in a set of scores 0 Relative frequency proportion of scoresrf 0 frequency of a score relative to the total number of observations 0 obtained by dividing frequency of each score by total number of observations 0 Percentage frequencyf o f of a score rf of a score X 100 Grouped frequency distributions STEPS 0 Put the scores in order 0 find the smallest and largest scores in your data then subtract the two 0 calculate an approximate interval size by dividing the above difference by how many intervals you would likeusually 520 round that interval size up to some simple value if needed pick a starting value that is less than or equal to the smallest score try to make it a multiple of the interval size 0 now calculate the list of intervals Percentile 0 score at or below a certain percentage Percentile rank 0 the percent of scores in the distribution that are equal to or less than that score Important equations to know WHEN FINDING THE PERCENTILE RANK USE THE EQUATION BELOW PxchxxLifi N Px percentile rank of a score ofX ch cumulative frequency of scores up to the lower real limit of the interval containing X x the score for which the percentile rank is being found xL lower limit of the interval containing X i size of the class interval fi frequency of scores in the interval containing X N total number of scores in distribution WHEN FINDING PERCENTILE USE THE EQUATION BELOW Xp xL PNchfii Xp score at a specified percentile xL lower real limit of the interval containing the specified percentile P required percentile given as a proportion between 0100 N total number of scores in the distribution ch cumulative frequency of scores up to the lower real limit of the interval containing Xp fi frequency of scores in the interval containing Xp i size of the interval example Class Real midpoin tall f rf f of crf cf Interval Limits t y 4650 455505 48 4 08 8 50 100 100 4145 405455 43 8 16 16 46 92 92 3640 355405 38 12 24 24 38 76 76 x38 xL355 fi 3135 305355 33 10 20 20 26 ch 52 52 xL305 fi 2630 255305 28 8 16 16 16 ch 32 32 21 25 205255 23 3 06 6 8 16 16 1620 155205 18 2 04 4 5 10 10 1115 105155 13 2 04 4 3 06 6 610 55105 8 0 0 0 1 02 2 15 555 3 1 02 2 1 02 2 Find the percentile rank for score 38 P38 26l38 3555l1 2 P3864 50 Find the 50th percentile in the distribution P5 Xp 305 55016105 x35 Illustrating frequency distributions Histograms not the same as a bar graph but similar 0 uses height of vertical bars to show frequency 0 bars touch 0 middle of bar is at the midpoint 0 real limits are the edges of the bar 0 xaxis score 0 yaxis frequency Types of Measures 0 measures of central tendency o What is the typical score in the data set 0 measures of variability o How spread out the data is Mean 0 sum of the numbers then divide by the number of numbers Deviation o xmean Sum of SquaresSS Zxmeann2

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