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PSY 3801 Week One Notes

by: Naomi Terpening

PSY 3801 Week One Notes psy 3801

Marketplace > University of Minnesota > Psychology > psy 3801 > PSY 3801 Week One Notes
Naomi Terpening
U of M
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About this Document

These notes cover everything from lecture on September 9th as well as the material covered in the chapters of recommended reading from the text books. I am just starting out with typing my notes...
Introduction to Psychological Measurement and Data Analysis
Mark A Stellmack
Class Notes
Statistics, Vocabulary




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This 8 page Class Notes was uploaded by Naomi Terpening on Saturday September 10, 2016. The Class Notes belongs to psy 3801 at University of Minnesota taught by Mark A Stellmack in Fall 2016. Since its upload, it has received 244 views. For similar materials see Introduction to Psychological Measurement and Data Analysis in Psychology at University of Minnesota.


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Date Created: 09/10/16
PSY 3801 Week 1 Notes Class Notes Measurement and Measurement Scales Measurement:  assigning numbers and/or labels to observations Measurement Scale:  set of possible numbers and/or labels that can logically be assigned  to observations 3 Properties of Measurement Scales: 1.  Magnitude:  larger numbers mean larger quantities  2. Equal Intervals:  having equal intervals along the scale of measurement 3. Absolute Zero:  zero on the scale means the absence of the variable being  measured 4 Types of Measurement Scales (can be used to determine what type of test can be  performed on the data) 1.  Nominal a. Does not have any of the three properties of measurement scales b. Classification c. Examples:  sex, gender, color, genre, nationality d. Numbers associated with the data are identifiers rather than  measurements or quantities 2.  Ordinal a. Has magnitude b. Does not have equal intervals or an absolute zero c. Examples:  ranking or ratings d. Distance between 1 and 2 may be different than between 2 and 3 i. In a race, 2  place may be two seconds behind 1  place while  rd nd 3  place is ten seconds behind 2  place. e. Only has meaning based on the comparison f. Gives information about relative standing 3. Interval a. Has magnitude and equal intervals b. Does not have an absolute zero c. Example:  temperature i. 0 degrees does not mean the absence of heat ii. This means that negative numbers are possible on the scale d. Cannot form meaningful ratios between measurements because of the  absence of an absolute zero 4. Ratio a. Has all three properties of measurement scales b. Example:  salary, weight c. Can form meaningful ratios i. “twice” or “half” actually mean something when discussing the measurements ii. This is possible because of the absolute zero Useful acronym for remembering these different scales is NOIR Variables vs. Constants Variable:  general characteristic that can vary  Constant:  characteristic or quantity that will not change its value within a certain context Summation Notation:  symbols that stand for mathematical terms or practices (Note:  I am increasing the font size for your viewing convenience in this section) n:  number of observations in a study; indicates the size of a sample x:  the variable i:  individual subject number; refers to one particular case xi  the variable result for a particular subject or case ∑:  Greek letter sigma; sum or add all together n ∑ x i :  =1 to n, add x; so if n=5, this means add x +x +x +x +1 2 3 4 5 i=1 ∑x :  simplified notation of the above; add all x­values ∑x :  square x­values and then add those results; “the sum of the squared x­ values” (∑x) : add all the x­values and then square that results; “the sum of the x­ values squared” y:  second variable ∑xy:  multiply each x­value by its corresponding y­value and then add the  results (∑x)(∑y):  multiply the sum of all the x­values by the sum of all the y­values Statistics Unplugged Chapter 1 (Note:  This chapter mainly covered vocabulary.) Variable:  any characteristic that can vary Data:  information about the variables Data Set:  a collection of information; a collection of data Data Points:  singular pieces of information which combine to form a data set; one data point  refers to one observation or one case Data Distribution:  a list of the values of a variable in a data set Frequency Distribution:  a table or graph that shows the frequency of a response in a data set Population:  collection of all possible cases in a study that is constantly changing Sample:  a portion of a population used in a study A sample is representative if t mirrors the population in important respects. Descriptive Statistics:  summarize or describe data Inferential Statistics:  the practice of using statistics to make inferences about a population This inference is hindered by sampling error, requiring more mathematical work to be  done for an inference to be deemed accurate. Statistic:  a characteristic of a sample Parameter:  a characteristic of a population Levels of Measurement: Nominal Level of Measurement:  simplest level based on categories that must be  mutually exclusive and collectively exhaustive Mutually exclusive:  each case can be placed into one and only one category Collectively exhaustive:  categories account for every possible response Ordinal Level of Measurement:  based on categories that can be placed in a meaningful  order Interval Level of Measurement:  base on an underlying scale of equal intervals If a zero point is present, it is an artificial zero point used as reference Ratio Level of Measurement:  all the characteristics of the interval level in addition to the presence of a legitimate, zero point Legitimate zero point:  zero as a data point actually means you have zero of  something Interval/Ratio Level of Measurement:  term used to refer to interval or ratio levels of  measurement as the difference is usually insignificant in statistical analysis These levels of measurement help determine the correct method of analysis Behavioral Sciences Stat 2 Chapter 1 Section 1:  Learning About Statistics Why do you need to understand statistics?  To work with the data obtained in research  To understand the research behind psychology Goals of the course:  Feel familiar with different research approaches  Know when it is appropriate to use a particular formula or equation  Know how to comprehend and interpret data Section 2:  The Logic of Research Samples and Populations  Population:  entire group of individuals to which a law of nature applies;  infinitely large  Sample:  relatively small part of the population that is intended to  represent the population as a whole  Participants:  individuals in the sample  Samples must represent the population accurately in order for the data to  be considered applicable to the population Understanding Variables  Variable:  anything that can have more than one score or outcome; refers  to the characteristic that is being measured usually  Quantitative Variable:  a variable in which the measured score reflects an  amount  Qualitative Variable:  a variable in which the measured score reflects a  category or classification Section 3:  Understanding Relationships Relationship:  a pattern that reflects consistent changes in one variable as another variable is being manipulated The Consistency of a Relationship  Relationships can be perfectly consistent; occurs when each x­value has  one and only one y­value  More often, relationships are less consistent and show a tendency rather  than a perfect association When No Relationship is Present  No pattern is evident  Results are too similar for each x­value to determine a relationship Section 4:  Applying Descriptive and Inferential Statistics  Descriptive Statistics:  procedures for understanding sample data o Usually involves a single calculated score that tells you something about  the data for the sample o Can also describe a relationship  Inferential Statistics:  procedures that help us determine if the data measured from the sample can be applied to the population Statistics versus Parameters o Statistic:  number that applies to the sample o Obtained from descriptive statistics o Symbolized by a letter from the English alphabet o Parameter:  number that applies to the population o Obtained from inferential statistics o Symbolized by a letter from the Greek alphabet Section 5:  Understanding Experiments and Correlational Studies o Design:  the layout of a study o Determines the type of statistical procedures necessary Experiments o Experiment:  a study in which a researcher manipulates one variable and  measures another to see if a relationship is produced o Independent Variable:  the variable that is manipulated o Condition:  a subcategory of the independent variable that creates the  situation being studied o Example:  if the independent variable was amount of time, the  condition would be either 1 hour, 2 hours, 3 hours, etc. o Dependent Variable:  the variable that is expected to change based on the  independent variable o This is the variable that is being measured for each condition Correlational Studies o Correlational Study:  a study in which no variable is manipulated; a  researcher measures two variables to determine if a relationship already  exists o Observing rather than manipulating o Correlational studies and experiments require different statistical  procedures Section 6:  The Characteristics of Scores o The type of scores present also determines the type of procedure o Different kinds of numbers require different math The Four Types of Measurement Scales o Nominal Scale:  measurement scale that categorizes rather than measures o Scores of numbers are used for identification and do not refer to an amount or value o Ordinal Scale:  measurement scale that indicates a ranking o Can be used to order scores o Indicate a relative amount; amount that separates each rank may  not be equal o No value of zero o Interval Scale:  measurement scale that measures an actual quantity of a  variable with possible scores being equally far apart o Zero on this scale doesn’t mean that none of the variable is present o Ratio Scale:  measurement scale with the qualities of an interval scale but  with a true zero in which none of the variable is present o Presence of a true zero on the scale allows for ratios to be  meaningful  In other words, when there are scores of 4 and 2, it makes  sense to say the 4 score is twice as much as the 2 score Continuous versus Discrete o Continuous Variable:  a variable in which measurements including  fractions or decimals make sense o Discrete Variable:  a variable in which fractional or decimal amounts do  not make sense


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