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Psych 2220 Ch. 1-5 Lecture Notes Bundle

by: Carolyn Kwon

Psych 2220 Ch. 1-5 Lecture Notes Bundle PSYCH 2220 - 0020

Marketplace > Ohio State University > Psychlogy > PSYCH 2220 - 0020 > Psych 2220 Ch 1 5 Lecture Notes Bundle
Carolyn Kwon

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For Psych 2220 with Dr. Joe Roberts. Midterm #1 covered chapters 1-5.
Data Analysis in Psychology
Joseph Roberts
Psychology, Data Analysis, Statistics, Lecture Notes, bundle, Chapter 1-5, ohio state university
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This 7 page Bundle was uploaded by Carolyn Kwon on Monday February 29, 2016. The Bundle belongs to PSYCH 2220 - 0020 at Ohio State University taught by Joseph Roberts in Spring 2016. Since its upload, it has received 29 views. For similar materials see Data Analysis in Psychology in Psychlogy at Ohio State University.


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Date Created: 02/29/16
Date: 1/13/16  Chapter 1    Descriptive vs. Inferential Statistics  ­ Descriptive­ computed values that allow us to describe a set of data we have  ­ Examples? “the average is…” “the median is…” “the range is…”  ­ Inferential­ values that let ugeneralize​  beyond our sample to its population  ­ Allows us to infer ​characteristics of a population  ­ “63 out of 100 students” [descriptive] vs. “63% of all students” [inferential]  ­ *make the assumption our sample ​ is representative of the overall population    Samples vs. Populations  ­ Population­ an entire set of observations about which we’d like to know something  ­ Parameters­ are the values that summarize a population  ­ S​ mple­ actual observations used in a study  ­ Statistics­ are the values that summarize a sample  ­ Difference between population and sample:   ­ population of the world = all living people  ­ sample is a subset = college students are a subset of all living people  ­ **an observation used in research could potentially be a population, and potentially a  sample = depends on how you are using the data    Variables  ­ observations that can take on a range of values  ­ example: reaction time in the stroop test (saying the word that is written, not color written in, etc)  ­ stroop test variables: reaction time, accuracy of answers, which person, font color, etc.  ­ relevant variables vs. not­relevant variables  ­ discrete vs continuous  ­ discrete­ variables that can only take on specific values (e.g. whole numbers)  ­ how many letters are in your name?  ­ does the text color match the word color?  ­ continuous­ variables that can take on a full range of values  ­ how tall are you?  ­ the temperature outside?    Scales of Measurement  ­ N.O.I.R.­ another system for identifying variable scales  ­ Nominal­ unordered category groups (i.e. religion, gender)  ­ O​rdinal­ ordered or ranked values (i.e. 1st place 2nd place 3rd place in a track meet)  ­ Interval­ ordered and uniformly scaled (i.e. )  ­ Ratio­ ordered and uniformly scales, with true zero point (i.e. age)          Date: 1/16/16  Types of Variables  ­ Discrete ­ variables that can ONLY take on specific values  ­ Continuous ­ variables that can take on a full range of values    Scales of Measurement  ­ N.O.I.R. ­ 4 tier system for identifying variable scales  ­ Nominal ­ meaningful comparators: = or ≠  ­ Ordinal ­ meaningful comparators: = ≠ < > ≤ ≥  ­ Interval ­ meaningful operations: + ­  ­ Ratio ­ meaningful operations: + ­ ÷ ✕  ­ Examples:  ­ Interval vs. Ratio: 80℉ ≠ 2 x 40℉ (40℉ + 40℉ = 80℉) But 300°K = 2 x 150°K  ­ Type of cookies = Nominal  ­ Ranking of fav cookie = Ordinal  ­ Rating of cookie tastiness = Interval  ­ Mass of cookies or quantity of cookies eaten = Ratio    Chapter 2    Displaying Data  ­ Frequency Distributions  ­ Raw vs. Summarized Data  ­ trade offs: we can see all of the data but it's hard to understand or we can summarize data  but lose some specifics  ­ Stem­and­Leaf Plot  ­ a graphical display of data that can reserve orginal info of the raw data while still aiding  visualization  ­ leading digits = stem  ­ trailing digits = leaf  ­ 5 | 0 2 3 (i.e. 50, 52, 53)  6 | 0 0 0 2 2 3 3 3 4 4 5 5 5 5 5 5 5 6 6   7 | 2 2 3 3 5 5 7 7 8 8   ­ Histograms  ­ Pie Charts?! = not a good representation    Date: 1/20/16  Displaying Data [continued]  ­ Scatter plot: used to plot one variable against another variable  ­ Bar graph: useful for comparing continuous variable across multiple values of a categorical variable  ­ Line graph: useful for continuous data  (General rules for displaying data)    Statistical Analysis Programs:  ­ Excel: multipurpose  ­ SPSS: menu­driven; lots of output  ­ R: syntax­driven;   ­ SAS: syntax­driven;   Psych 2220 HW #1 Notes:    Continuous​  variable = data that you would have to count for ‘forever’ ; can take on range of values ; e.g. how  tall (constantly growing), temperature outside (constantly changing)  Discrete​ variable = data that has a limit ; only take on specific values ; e.g. whole numbers, # of letters in  people’s names, if text color matches word color    [N­O­I­R variables]  Nominal​  = data that is unordered and can be put into categories ; (e.g. gender, eye color, type of house,  type of pet, genotype)  Ordinal​ = data that can be ranked (but do not know the interval between each rank) ; in order & rankable  values ; (e.g. 1st, 2nd, 3rd, 4th place)  Interval = data that is uniformly scaled (in between two points) ; in order and uniformly scaled  Ratio​ = data that is uniformly scaled and has a ‘meaningful’ zero point (so no negative numbers) ; in order  and uniformly scaled and has a zero point (where zero means non­existent)    Nominal ­> always discrete, never continuous  Ordinal ­> always discrete, never continuous  Interval ­> sometimes discrete, sometimes continuous  Ration ­> seldom discrete, almost always continuous    Independent​  variable = stands on its own and is not affected by anything you, as a researcher, do.  (independent = input = x­axis)  Dependent​  variable = the thing that you are expecting will change / trying to change  (dependent = output = y­axis)    Skewed Distributions:  Negative​  (Tail is on the left)Positive (Tail is on the right):    Normal​  and/or​Symmetrical:​     *Ceiling​ effect Floor​ effect = when abnormally high (ceiling) or low (floor) data causes a skew  Date: 1/22/2016  Chapter 4    Formula to compute the arithmetic average of N values aka the Mean:  N   (∑ X i ( X )i i=1   N   or   N     Measures of Central Tendency:  ­ Central Tendencies:  ­ Arithmetic Mean​ : sum of values divided by number of the values  ­ Median​ : middle value of a set, when the set has been arranged in increasing order  ­ Mode​ : the most commonly/frequently occurring value in the set    ­ Mean:​  Advantages & Disadvantages:  ­ Advantages:   ­ more stable indicator across samples (than the median or mode)  ­ plus it has a cool math formula  ­ Disadvantages:   ­ requires assumption of interval scale properties for valid inferences about the  meaning of an arithmetic mean  ­ biased by extreme scores (outliers) ­ the sample mean is a less stable indicator of  central tendency when dealing with asymmetric population distributions  ­ Median:​  Advantages & Disadvantages:  ­ Advantages:  ­ uninfluenced by occasional extreme scores (as compared with the mean)  ­ can be better than the mean for describing o ​rdinal data  ­ Disadvantages:  ­ difficult to use in formulaic calculations  ­ Mode:​  Advantages & Disadvantages:  ­ Advantages:  ­ always reflects a ​real (and likely) outcome. (if you want to predict real outcomes)  ­ best for describing ​nominal​ data  ­ Disadvantages:  ­ volatile/ unstable indicator because it can jump around quite a bit from sample to  sample or with increasing sample size  ­ When to use which?  ­ If your data areInterval​ = use the mean  ­ Ordinal​ = medien (should not use mean)  ­ Nominal ​ = mode (can’t use median or mean)  ­ Ratio​ = mean (but also have more choices)    ­ Variability​: degree to which individual scores cluster around or deviate away from the central  tendency of the group of scores (aka ​ dispersion)​  ­ e.g. Students’ grades (with a B­ class average, how many earn A?),   ­ Race times (amateur vs pro competitions),   ­ Game scores (offensive vs defensive style),   ­ Attitude ratings across time (strong vs weak attitudes)  ­ Other Terms:  ­ Range​ : distance from lowest to highest score (different from scale endpoints!)  ­ Outlier​: score(s) that stands out from most other scores  ­ Quartiles​: points at which data are carved into sequential groups comprising 25% each  (along with median) (e.g. 0 ­ 25 ­ 50(median) ­ 75 ­ 100)  ­ Interquartile range =IQR = Q3 ­ Q1    ­ Variance  2​ 2 ​ ­ Population Variance (SD​  or  σ): how much scores vary around the mean within a  population  ­ calculated by summing the squared deviations from the mean, then dividing by the  # of observations (as if computing the average deviation)  ­ because we very often don’t have access to the population, we won’t need to compute the  population’s variance very much  ­ instead we will compute ​sample​  variance (s) and use that as an estimate of  population variance  ­ Population variance =   σ     ∑ (x−m) 2 2     σ =   N   ­ Standard deviation = square root of variance =  σ     ∑ (x−m)2 2     σ  =   √    =  N   √   ***Memorize these formulas!!!          Date: 1/25/2016    REVIEW:  ­ standard deviation is square root of variance  ­ both are ways of describing how far our scores tend to stray from the mean of the scores  ­ how do they differ? standard deviation is more intuitive (more accurate?) for understanding the  variability      Chapter 5    Basic Concepts of Probability  ­ Event​ : the outcome of a trial  ­ e.g. coin flip is the outcome heads or tails?  ­ Probability​ : the number of times an event occurs divided by the total number of times it could have  occurred  ­ Notation: p(event)  ­ Joint Probability​ : the probability of the co­occurrence of two or more events  ­ Denoted as p(A,B) or p(A ∩ B) for events A and B  ­ ∩ symbol is “intersection” meaning only the set of situations where both A & B occurred;  only the overlap of A & B events co­occurring    ­ Mutually exclusive events​ : occurrence of one event rules out occurrence of other event  ­ e.g. coin flip: can only land heads or tails, not both  ­ e.g. can be HIV+ or HIV­, can't be both  ­ Independent events  ­ Additive Law​ : IF events A and B are mutually exclusive​, THEN the probability of A OR​ B = sum of  each probability  ­ p(A ∩ B) = P(A) + P(B)  ­ e.g. probability of heads or tails = P(heads) + P(tails)  ­ first question to ask: is it mutually exclusive?    ­ Multiplicative Law​ : IF events A and B are independent​, THEN the probability of joint occurrence of  A ​AND ​B = product of individual probabilities  ­ p(A ∩ B) = P(A) x P(B)  ­ e.g. what is probability of rolling two sixes with a pair of dice? If dependent = p(“6”) x p(“6”)  ­ first question to ask: are they independent of each other?  ­ sampling with or without replacement  ­ works similarly forN​ independent events:  N p(A​ 1​x A​2​x A​ 3​… A​ N​  =  ∏ p(A)  i=1   Date: 2/1/2016  Exam Content: Chap 1­5  on Monday Feb 8  ● NOIR variable scale types  ● Descriptive vs Inferential stats  ● Random sampling  ● Summation notation and arithmetic  ● Distributions & properties  ● Appropriate ways to graph different types of data  ● Central tendency measures: properties, strengths, weaknesses, calculations  ● Variability measures: properties, strengths, weaknesses, calculations  ● Probability: terms & concepts, laws, calculations (some not in textbook)  ● Hypothesis testing: terms & concepts, notation  ● *Methodological terms & concepts ­ not on exam*    ***BRING QUESTIONS TO CLASS      Date: 2/5/2016  Exam Review:    ­Joint probability: p(A ⋂ B) “intersection of A and B”  ­Conditional probability: p(A | B) “probability of A given B” or B given A  ­Union: p(A ∪ B) “A union B” probability of both A and B    Conditional:  ­ p( Type II Error ) = P( Fail To RejecO​as False ) =β “beta”  ­ p( Reject | O​as False ) =​ − β= “Power​ ”    


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