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Lectures 5 and 6

by: Brenna Eisenberg

Lectures 5 and 6 PSY 313

Brenna Eisenberg
GPA 3.535

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About this Document

Material from lectures 5 and 6.
Intro. To Research Methodology
B. Seymour
Class Notes
Psychology, bias, Lecture Notes, Research Methodologies, research, Sampling, stability
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This 2 page Class Notes was uploaded by Brenna Eisenberg on Tuesday October 4, 2016. The Class Notes belongs to PSY 313 at Syracuse University taught by B. Seymour in Fall 2016. Since its upload, it has received 3 views. For similar materials see Intro. To Research Methodology in Psychology (PSYC) at Syracuse University.

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Date Created: 10/04/16
Lecture 5- survey research Wednesday, September 21, 2016 11:43 AM • Instead of observing behavior and inferring someone's state of mind, they simply ask. • Ex- course evaluations, depression surveys • Structure: ○ Open with non-threatening interesting question-- good for an open ended question ○ Respondent will be reacting to the questions. Ex- pro-life vs pro-choice issue. Don’t want your bias to be visible ○ General questions before specific ones ○ Routine demographics at end of survey. They are easy and people won't be affected by fatigue • Open ended questions: ○ Ex- are you an emotional person? • Restricted choice questions: ○ Ex- Give options ranging from "not emotional at all" to "I am an extremely emotional person" ○ Quick and easy to analyze ○ Provides quantitative data ○ Answer is biased by set of provided answers ○ Person may give an answer even though they do not understand the question ○ 4 types:  Likert- strongly agree- strongly disagree  Semantic differential- ex- president obama is weak------strong. Intelligent-----unintelligent. Categories of meaning differentiating between two things. Give people a statement to complete or categorize. Give a category and its opposite  Quantitative- how many times do you do something? Give numbers/ ranges  Categorical- ex- my favorite color is: (give options of colors) • Potential problems with surveys: ○ Using appropriate vocab- people need to understand what you are asking. Consider your audience ○ Avoid words with unwanted emotional baggage. Do not want to offend people taking the survey ○ Avoid leading questions. May push a person's answer in a certain way. Ex: do you agree that…? ○ Avoid tactless questions. ○ Be clear in how you are asking things. ○ Avoid ambiguous answers. Give more specific options for answers. • Response set: people tend to pick a response and stick with it if possible (give a rating of agree to every question) independent of the questions themselves ○ Detect by using positive and negative statements ○ The teacher was organized- strongly agree--------strongly disagree ○ The course is poorly structured- strongly agree--------strongly disagree • Establish a frame of reference- want to know why someone answers the way that they do ○ Ask broad question to establish the reference frame ○ Ex- do you find SU to be a good school? Why? • Memory is easily altered ○ Dream study, Mazzoni et al ○ Gave subjects a questionnaire about events that happened in their lives. Chose people who havent been lost in a mall as a kid ○ Called 10-15 days later for what they thought was a different study on dream interpretation. Told therapist about recent dream. Therapist suggested dream was a repressed memory of being lost in the mall as a kid ○ Later completed same questionnaire as before, 60-80% of people now reported being lost in a mall as a kid Lecture 6- sampling • Population of interest (college students)-----> narrows into sample (people who took the survey) • Sample- how we select people from the population • Assignment- how you take the sample and put them into conditions of the experiment Research methods Page 1 • Developing a sampling plan can help establish representativeness to ensure our sample is representative of the population • Representative is measured by bias and stability • Bias- sample differs from population on important dimensions. We have inaccurately sampled the data ○ Systematic difference between sample and population • Stability- how much noise is in the data? Unstable = not reliable. Spread/variance of the sample. ○ Improve stability with large sample size Unbiased, unstable Biased, stable Biased, unstable Unbiased, stable • Want no/low bias and high stability • Large sample size means more certain that your sample represents the population Research methods Page 2


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