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PSY 3801 Exam 1 Study Guide

by: Naomi Terpening

PSY 3801 Exam 1 Study Guide psy 3801

Marketplace > University of Minnesota > Psychology > psy 3801 > PSY 3801 Exam 1 Study Guide
Naomi Terpening
U of M

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This my study guide for the first exam. They are organized according to main concept and have a highlighting system separating key terms from key concepts. They include material covered in lectur...
Introduction to Psychological Measurement and Data Analysis
Mark A Stellmack
Study Guide
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This 5 page Study Guide was uploaded by Naomi Terpening on Friday September 16, 2016. The Study Guide belongs to psy 3801 at University of Minnesota taught by Mark A Stellmack in Fall 2016. Since its upload, it has received 285 views. For similar materials see Introduction to Psychological Measurement and Data Analysis in Psychology at University of Minnesota.


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Date Created: 09/16/16
PSY 3801 Exam 1 Study Guide key concept key term Measurement Scales Properties of Measurement Scales: 1.  Magnitude:  larger numbers mean larger quantities 2. Equal Intervals:  having equal intervals between values along the scale 3. Absolute zero:  zero on the scale means the absence of what is being measured 4 Types of Measurement Scales 1.  Nominal:  measurement scale that works by a system of classification a. Has none of the three properties b. Examples:  sex, gender, color, nationality, etc. 2. Ordinal:  measurement scale that orders the variables but does not give any  other information a. Has magnitude but not equal intervals or absolute zero b. Only has meaning by comparison because the intervals are different c. Examples:  ratings and rankings 3. Interval:  measurement scale in which the values can be ordered and have  equal intervals but in which ratios between values have no meaning a. Has magnitude and equal intervals but no absolute zero b. Without absolute zero, there can be no ratios c. Example:  temperature 4. Ratio:  measurement scale in which ratios between different values are  meaningful a. Has all three properties of measurement scales b. With the presence of absolute zero, meaningful ratios can be made i. Twice or half makes sense c. Examples:  Salary or weight Types of Variables Independent vs. Dependent Variables  Independent variable:  primary variable that is manipulated in an experiment  Dependent variable:  variable that is expected to change based on the independent variable Discrete vs. Continuous Variables  Discrete variable:  variable whose values can only assume a finite number of options between any two points on the measurement scale o No values between those indicated o No fractions or decimals  Continuous:  variable whose values can assume an infinite number of options between any two points on the measurement scale o Values can be split into fractions and decimals Sample vs. Population Sample  Sample:  small group out of the population that is being studied o Results about this group are called statistics o Meant to be representative of the population Population  Population:  the entire group that applies to a certain study or characteristic o Usually much too big to be studied on its own o Results that are applied to the population are called parameters Types of Studies Descriptive Methods  Descriptive methods:  research methods that are meant to describe behavior  Observational method (naturalistic observation):  study in which no manipulation occurs;   study   in   which   the   researcher   simply   observes   and   records   his observations  Case study:  study based on one or a few rare cases in which a researcher tries to get a detailed view of a rare situation or condition  Survey method:   study based on questionnaire and surveys that relies on the honest replies of those involved Predictive (Relational) Methods  Correlational method:  study in which two variables are measured without any manipulation in order to see if a relationship is already present o Correlation does not mean the same thing as causation  Quasi­experimental method:  study in which variables are manipulated without random assignment Experimental Method  Experimental method:  study in which one variable is manipulated to determine if this affects another variable; includes random assignment o Random assignment assures that the groups being compared are similar enough to allow reliable conclusions to be made. Measures of Central Tendency  Central tendency:  general term for the typical score in a distribution o Meant to be representative of the distribution in some way Mean  Mean:  arithmetic average  How to compute:  add all values together and then divide by the number of cases  Notation:   o Computation: (∑x)/n o Mean of a sample:  x  o Mean of a population:  µ  How is it a measure of central tendency?   o Balancing point:  values above and values below the mean are equal to each other  Properties of a mean: o Changing a score in a set of scores changes the mean o Adding or removing a score from the set usually changes the mean o Extreme scores can affect the mean drastically Median  Median:  score that divides the distribution in half so that there is an equal amount of scores above and below the median  How to compute:   o Order the values from least to greatest o Add 1 to n and divide by two to find the position of the median o If position number is a whole number, go back to your ordered list and find the value that correlates to the position found in previous step o If position number is not a whole number, take the values in the position above and below the position number and average them for your median.  Notation:  M d  How is it a measure of central tendency? o Gives the middle score  Properties of the median:   o Extreme scores do not change the median as long as the n value is the same o Scores in the middle determine the median Mode  Mode:  the value that occurs the most in a distribution  How to compute:  Find the value that is reported the most and that is your mode  Notation:  usually just spelled out  How is it a measure of central tendency? o Gives the value that is reported the most, which is a way of being a typical response o Most appropriate for nominal scales  Properties of the mode: o Can be unimodal, bimodal, or multimodal Measures of Variability  Variability:  term that describes how spread out the data in a distribution is Range  Range:  the difference between the highest and lowest data points  How to compute:  see definition  Notation:  spelled out  How is it a measure of variability? o It tells how spread out the data is. o Casual statement of the variability Variance  Variance:   measure of variance found by taking the sum of the squares and dividing it by n­1  How to compute:   o Definitional Formula Method  Find the mean.  Find the sum of the x­values minus the mean.  Square this sum.  Divide by n­1. o Computational Formula Method  Find the sum of the squared x­values.  Square the sum of the x­values.  Divide the value of the second step by n.  Subtract the result of step 3 from the result of step 1.  Divide the result from step 4 by n­1  Notation: o s 2 o Definitional Formu2a Σ(x−´ x)  n−1 o Computational Formu2a 2 (Σx) Σx − n  n−1 Standard Deviation  Standard deviation:  measure of variance found by computing the square root of the variance  How to compute:  square root the variance  Notation:  2 o s=√s


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