New User Special Price Expires in

Let's log you in.

Sign in with Facebook


Don't have a StudySoup account? Create one here!


Create a StudySoup account

Be part of our community, it's free to join!

Sign up with Facebook


Create your account
By creating an account you agree to StudySoup's terms and conditions and privacy policy

Already have a StudySoup account? Login here

Chapter 1 Notes

by: Nicole Creekmore

Chapter 1 Notes PSYS 241

Nicole Creekmore
GPA 4.0

Preview These Notes for FREE

Get a free preview of these Notes, just enter your email below.

Unlock Preview
Unlock Preview

Preview these materials now for free

Why put in your email? Get access to more of this material and other relevant free materials for your school

View Preview

About this Document

Notes from Chapter 1 in psychology Statistics
Dr. Tagler
75 ?




Popular in Statistics

Popular in Psychology (PSYC)

This 8 page Bundle was uploaded by Nicole Creekmore on Thursday September 15, 2016. The Bundle belongs to PSYS 241 at Ball State University taught by Dr. Tagler in Fall 2016. Since its upload, it has received 6 views. For similar materials see Statistics in Psychology (PSYC) at Ball State University.

Similar to PSYS 241 at BSU


Reviews for Chapter 1 Notes


Report this Material


What is Karma?


Karma is the currency of StudySoup.

You can buy or earn more Karma at anytime and redeem it for class notes, study guides, flashcards, and more!

Date Created: 09/15/16
Possible Hypotheses ● 1­THC affects performance on a vigilance task ○ THC improves performance ○ THC impairs performance ● 2­ THC does not affect performance on a vigilance task ○ The null hypothesis  ■ Null­no effect ● What type of evidence would support 1? 2? Conduct the experiment and collect the data… THC PLACEBO 12 17 15 16 16 12 14 21 15 17 12 16 9 19 11 18 MEAN=13 17 DV = # correct responses on the vigilance task The Scores vary! ● It’s very likely that participants will score differently from one another on the  vigilance task no matter what ● Why? ○ Individual differences? ○ Chance, error? ○ A real treatment effect?  ● So, statistics provides us with tools to answer “why” questions Scales/Levels of Measurement Stevens (1946) ● Nominal ● Ordinal ● Interval ● Ratio Nominal Scales ● The measurement of variables that are qualitative, categorical in nature ● Using words or numbers as arbitrary labels for classification (identification): ○ Gender: 1=male 2=female ○ Race: 1=caucasian 2=african american 3=hispanic 4=asian/pacific islander 5=native american ○ Football Jersey numbers ■ See Lord (1953) On the Statistical Treatment of  Football Numbers  Ordinal Scales ● Measuring some quantity (magnitude), but only in terms of rank order ○ Athletic Standings (1st place, 2nd place, etc.) ○ Any “top 10” list ○ Rating employee performance by forced­choice comparisons Interval Scales ● A quantity being measured on a scale with equally sized intervals but no true  zero point ○ Fahrenheit, Celsius temperature scales ○ Psychological tests (we assume) ■ IQ, personality, attitudes, etc.  Ratio Scale  ● A quantity being measured on a scale with equal intervals AND a true zero  point ○ Kelvin temperature scale ○ Physical qualities: height, weight, length ○ Time ○ Income Another Distinction: ● Continuous scale measures: ○ Can exist in a theoretically infinite degree of precision (smaller and smaller fractions) ■ Age, time, weight, height ● Discrete scale measures: ○ Numbers separated by real gas: ○ Can only exist in “whole units” ■ Family size, gender, income ○ In discrete scales, the numbers HAVE TO STOP SOMEWHERE ORGANIZING DATA ● Frequency, distributions, histograms ● Looking at your data! Why “look at” your data? ● To catch errors from data entry ○ E.g., impossible values & outliers ● To check for violations of statistical assumptions ● Sometimes a simple graph or table provides the answers you seek ● Pictures are worth a thousand words ○ Often easier to understand and communicate your findings  graphically Daily high temperatures (in Fahrenheit) for 31 days during the month of october 2004 in Small  Town, NE 66.8 70.5 89.8 81.8 78.2 86.5 71.5 75.9 74.2 70.5 63.0 82.7 79.5 77.4 89.4 92.0 74.8 71.0 66.4 51.2 80.4 78.1 86.5 75.7 75.0 86.5 66.8 54.5 72.8 78.1 76.6 ● Can make a frequency distribution table, putting the data in order with frequency  totals (how often did the specific data occur) ○ N = number of scores or sample size ○ Ungrouped frequency distribution tables show each number ○ Grouped frequency distribution tables gives ranges of data ● Excel­generated Histograms are bar graphs that show grouped frequency  distribution Central Tendency Assignment due Thursday­show work Measures of Central Tendency Mean, Median, Mode Descriptive Statistics ● Statistics that are used to describe or summarize the characteristics of a set of  scores ● Two types we will cover (but not the only 2): ○ Central tendency ○ Variability (next chapter) Measures of Central Tendency ● Descriptive stats that summarize a set of scores by determining where the  “center” of a distribution is ● The identification of a “single point” around which most (or at least most typical)  scores are located ○ These measures indicate “location”, and can be viewed as “point  estimates” Mode ● Most frequently occurring score(s) ○ Unimodal: 1 mode ○ Bimodal: 2 modes ○ Multimodal: 2+ modes ● With NOMINAL data, this is the only measure of central tendency that works ○ But it can also be used with other scales of measurement (often  good to calculate all 3 measures of central tendency) ● Limitations ○ Not useful in rectangular (uniform) distributions ○ Does not take into account any scores other than those highest in  frequency ■ So, in the following data, the mode doesn’t seem to be a good summary measure: 98 98 98 90 84 84 83 81 78 77 77 72 71  68 68 66 64 62 ■ Mode=98 ■ Median=77.50 ■ Mean=78.72 Median ● Middle score: value at the 50th percentile ● First rank order the data ○ Odd number: middle position ○ Even number: compute the mean of the 2 middle scores for a  good estimate ● Particularly useful when dealing with a skewed distribution (e.g., income) ● Drawback is, like the mode, it doesn’t use all of the information in the set of  scores Mean ● Arithmetic mean ● Arithmetic average ● Interval or ratio data needed ● “The sum of the somethings divided by the number of somethings” ● x=whole entirety of the scores, N=number of scores, divide the sum of all scores  by the number of scores ● The mean has desirable mathematical properties: it uses all of the scores ● When the distribution is skewed, the mean gets “pulled” toward the tail, much  more so than the median Skewed Distributions ● Skewed distributions have more scores on one end and “tails” of fewer extreme  scores ● Negatively skewed distribution, the median exceeds the mean ● Positively skewed distribution, mean exceeds the median Positively or Negatively Skewed? ● A distribution of test scores on an easy test, with most students scoring high and  a few students scoring low ○ Tail would be on left, negative ● A distribution of ages of college students, with most students in their late teens or early twenties and a few students in their fifties or sixties ○ Tail would be on right, positive ● A distribution of loose change carried by classmates, with most carrying less than $1 and with some carrying $3 or $4 worth of loose change ○ Tail would be on right, positive ● A distribution of the sizes of crowds in attendance at a popular movie theater with most audiences at or near capacity ○ Tail would be on left, negative ● IF THERE IS A LOT OF SKEW, TRUST THE MEDIAN, IF THERE IS NOT A  LOT OF SKEW, USE THE MEAN, ONLY USE THE MODE WHEN THERE IS  NOMINAL DATA Measures of Variability (variation) ● Descriptive statistics that indicate the extent to which the scores in a distribution  tend to differ or vary ● Measures of “spread” ● How much are scores dispersed?  ● Despite having the same mean, sets of scores differ from each other in important ways ○ Measures of central tendency DO NOT provide any information  about degree of spread (variability) in a set of scores The Range ● The difference between the lowest and the highest in a distribution ○ Range=highest score­lowest score ●  Provides information only about the maximum amount of spread among scores ● Thus, it is overly influenced by extreme values ● Not widely used because it does not provide information about the typical amount of spread among scores The Interquartile Range (IQR) ● Defines the range of values between the 25th percentile (1st quartile) and the  75th percentile (3rd quartile) ○ IQR=75th percentile­25th percentile ● Thus, the IQR completely avoids (ignores!) the influence of the most extreme  scores ● But what are percentiles? ○ A percentile is the score at which a given % of the scores in a  distribution fall at or below ○ The median is the 50th percentile ■ 50% scores at or below it ○ 75% of the scores are at or below the 75th percentile ○ 25% of the scores at at or below the 25th percentile ● IQR better than Range because not influenced by extreme scores ● But, like the range it is limited by the fact that it does not take all the scores into  account ○ (it just uses the middle 50%) We want to use all the scores ● Want a measure of the “typical” or “average” distance that the scores are spread  around the mean ● Mean distance ● How to compute a mean distance? ● Like any mean, we need to sum the “somethings” and divide by the number of  “somethings” ○ In this case, the “somethings” are distances  Distance is measured using  deviation scores ● Deviation scores: the distance that each score is from the mean of the set of  scores ● Simply computed by subtracting the mean from each score ● For any set of scores, the sum of the deviations from the mean equals zero ○ So it is not a useful way of measuring variability ○ How to “fix” this problem? “Get rid” of the negative signs Variance ● The sum of the SQUARED DEVIATIONS divided by the number of SQUARED  DEVIATIONS ● One problem with variance ○ Difficult to interpret ■ “Squared distances” ○ So, we can make it easier to interpret by taking the square root of  the variance Standard Deviation ● The SQUARE ROOT of variance ● Aka, the SQUARE ROOT of the sum of the SQUARED DEVIATIONS divided by  the number of SQUARED DEVIATIONS ● A problem ○ If you use the formulas foσ and σ² with sample data, the  answers tend to underestimate the population variability ■ A “biased” estimate ■ More often return values that are too small ○ Unbiased estimates ■ The “misses” average out to the desired target ○ The correction: N­1 ○ S2 is sample variance, s is sample standard deviation ○ Correction has a smaller effect as sample size gets bigger  S AND  σ ARE STANDARD DEVIATION,  S2 AND σ² ARE VARIANCE S FORMULAS HAVE N­1 `σ FORMULAS HAVE N Score Score­Mean (Score­Mean)² Sum of scores Sum of deviations  Sum of deviations² (SHOULD = 0) Standard Scores ● Indicate in standard deviation units how far a single raw score in a set of scores  deviates from the mean ● Z­scores: computed by subtracting the mean from a raw score, and dividing by  the standard deviation Uses of Standard Scores ● You have a single score in a set of scores and want to determine the relative  standing of the score: ○ Is this score good/bad, high/low relative to the rest of the scores? ● You want to compare two scores from two different distributions ○ Comparing apples to oranges  ● Why subtract the mean? ○ This is a deviation score! The numerator is a “distance” that the  raw score is from the mean ● Why divide by standard deviation? ○ This puts it into a “unit of 1”, reduces the denominator value to 1 Properties of z­scores ● If you convert a set of scores to z­scores: ○ Mean=0 ○ Standard Deviation = 1  ○ Variance = 1 ○ The shape of the distribution is the same as the shape of the  original scores ■ If a set of scores are skewed or kurtotic, converting  them to z­scores results in a set of z­scores that are identical in terms of  skewness and kurtosis ● IMPORTANT: Transforming to z­scores does not produce a normal distribution ● If raw data are normally distributed, then converting to z­scores results in a unit  normal distribution The Empirical Rule Restated in terms of z­scores ● In a unit normal distribution, 68.26% of the values are between a z­score of +1.0  and ­1.0 ● In a unit normal distribution, 95.44% of the values are between a z­score of +2.0  and ­2.0 ● In a unit normal distribution, 99.74% of the values are between a z­score of +3.0  and ­3.0 Z­tables ● Available in most statistics textbooks, pages 536­537


Buy Material

Are you sure you want to buy this material for

75 Karma

Buy Material

BOOM! Enjoy Your Free Notes!

We've added these Notes to your profile, click here to view them now.


You're already Subscribed!

Looks like you've already subscribed to StudySoup, you won't need to purchase another subscription to get this material. To access this material simply click 'View Full Document'

Why people love StudySoup

Bentley McCaw University of Florida

"I was shooting for a perfect 4.0 GPA this semester. Having StudySoup as a study aid was critical to helping me achieve my goal...and I nailed it!"

Allison Fischer University of Alabama

"I signed up to be an Elite Notetaker with 2 of my sorority sisters this semester. We just posted our notes weekly and were each making over $600 per month. I LOVE StudySoup!"

Bentley McCaw University of Florida

"I was shooting for a perfect 4.0 GPA this semester. Having StudySoup as a study aid was critical to helping me achieve my goal...and I nailed it!"

Parker Thompson 500 Startups

"It's a great way for students to improve their educational experience and it seemed like a product that everybody wants, so all the people participating are winning."

Become an Elite Notetaker and start selling your notes online!

Refund Policy


All subscriptions to StudySoup are paid in full at the time of subscribing. To change your credit card information or to cancel your subscription, go to "Edit Settings". All credit card information will be available there. If you should decide to cancel your subscription, it will continue to be valid until the next payment period, as all payments for the current period were made in advance. For special circumstances, please email


StudySoup has more than 1 million course-specific study resources to help students study smarter. If you’re having trouble finding what you’re looking for, our customer support team can help you find what you need! Feel free to contact them here:

Recurring Subscriptions: If you have canceled your recurring subscription on the day of renewal and have not downloaded any documents, you may request a refund by submitting an email to

Satisfaction Guarantee: If you’re not satisfied with your subscription, you can contact us for further help. Contact must be made within 3 business days of your subscription purchase and your refund request will be subject for review.

Please Note: Refunds can never be provided more than 30 days after the initial purchase date regardless of your activity on the site.