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by: Stephanie

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# Elementary Statistics Chapter 1 Day 2 Notes Psy 202

Marketplace > University of Mississippi > Psychology > Psy 202 > Elementary Statistics Chapter 1 Day 2 Notes
Stephanie
OleMiss
GPA 4.0

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These notes cover the second lecture on Chapter 1.
COURSE
Elementary Statistics
PROF.
Matthew Mervin
TYPE
Class Notes
PAGES
3
WORDS
CONCEPTS
Statistics, PSY202
KARMA
25 ?

## Popular in Psychology

This 3 page Class Notes was uploaded by Stephanie on Saturday August 27, 2016. The Class Notes belongs to Psy 202 at University of Mississippi taught by Matthew Mervin in Fall 2016. Since its upload, it has received 36 views. For similar materials see Elementary Statistics in Psychology at University of Mississippi.

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Date Created: 08/27/16
PSY 202 Elementary Statistics  Chapter 1:  Introduction and Background – Day 2 I. Population Samples and Sampling Techniques (cont’d) a. Sampling summary i. Population 1. Characteristics of a population are known as parameters a. These are represented by a Greek letter ii. Sample 1. Characteristics of a sample are known as a sample statistic a. These are represented by English letters II. Variables  a. Independent v. Dependent Variable i. Independent variable:  This is the variable that is manipulated in an  experiment ii. Dependent variable:  This is the variable that is measured in an experiment iii. In an experiment we try to see if the independent variable (IV) causes the  dependent variable (DV) b. Qualitative v. Quantitative Variables i. Qualitative 1. These variables are strictly categorical 2. They are used for labeling things ii. Quantitative 1. These are what we will mainly use in statistics 2. These variables has numerical value, so they are counting  something  c. Discrete v. Continuous Variables i. Discrete variables 1. These variables do not have a fractional amount ii. Continuous variables 1. These variables do have a fractional amount 2. They have an infinite number of values a. Ex:  8, 8 ¼ , 8 ½ , 8 ¾ , 9 III. Defining the constructs and selecting the variable a. Construct i. A construct is an idea that you cannot see directly ii. We infer things by observing people (body language, actions) 1. The problem with this is that behavior and thought processes often  do not match up iii. Operational definition 1. In order to create this, we take something that we cannot see and  turn it into something that we can measure b. Measuring our variables i. Ex:  Operational definition of love ( For this example, we will be using  The Notebook movie poster) ii. What can we use to measure love? 1. People are extremely close and have a lot of physical contact 2. Amount of time spent together 3. Dialogue:  Count the number of compliments or how many times  they say “I love you” c. Nominal Scales i. These scales are only used for categorical information d. Ordinal Scales i. These have categorical information and a natural scale (rank information) 1. Ex:  Say you have three peppers.  A hotter pepper is going to have  more heat than a hot pepper, and a hottest pepper is going to have  more heat than a hotter pepper.   ii. Ordinal scales will not tell the distance between ranks e. Interval Scales i. We use this scale for our analyses ii. This scale gives categorical and rank information as well as distance  between ranks  f. Ratio Scales i. We use this scale for our analyses as well ii. This scale cannot have negative numbers and 0 is the minimum iii. It gives categorical, rank, and distance information and measures and  absence iv. This scale allows things to be put into fractions g. The scales allow us to determine what type of analyses to use when measuring  data h. Scales of measurement in the social sciences i. Our minds do not process the scales the way they should be in real life 1. Ex:  There are three gas prices labeled \$3.49, \$3.59, \$3.69 a. The brain will think that the difference between \$3.59 and  \$3.69 is greater than the difference between \$3.49 and  \$3.59 i. Implications of measurement scales for statistics i. If you know what you are measuring then you know what scale/ analysis  to use ii. Reliability 1. In general, it is the ability to get the same results over and over  again 2. Changing results makes it difficult to make predictions and to  generalize to population iii. Validity  1. Validity is whether or not you are measuring what you intended to  measure 2. Ensuring validity a. We must come up with clear and concise operational  definitions b. We must also control variables so that they do not skew  with results i. Confounds:  Other variables that surround the  independent variable IV. A Brief History of Statistics a. Earliest use i. Statistics was first used in the caveman days 1. It was very simple and rudimentary 2. It did not have the ability to make future predictions b. Developments in mathematics and probability  i. Advances in calculus helped to create ways for predictability  c. Statistics in the modern era  i. Technology (i.e. computers) helped to advance statistics

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