Data Basics Quiz: Variables & Studies
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Questions and Answers

What is the primary characteristic of simple random samples (SRS)?

  • They randomly select cases from a population. (correct)
  • They require extensive voluntary participation.
  • They focus exclusively on clusters of observations.
  • They are often biased towards wealthy individuals.

    • Why might certain survey respondents offer strong opinions?

  • They typically are influenced by peers.
  • They are randomly chosen from various strata.
  • They often lack access to survey participation.
  • They are less likely to represent the general population. (correct)

    • What is a potential flaw in relying on volunteer responses for surveys?

  • Volunteers provide exclusively negative feedback.
  • There are usually fewer responses than expected.
  • Results may not reflect the entire population's views. (correct)
  • Everyone is equally likely to respond.

    • What does stratified sampling aim to achieve?

    <p>To represent subgroups proportionately within the population. (A)</p> Signup and view all the answers

    Which of the following statements accurately describes cluster sampling?

    <p>It involves selecting entire groups to draw conclusions about the population. (C)</p> Signup and view all the answers

    Which level of measurement allows for the ranking of categories?

    <p>Ordinal (D)</p> Signup and view all the answers

    Which of the following methods is typically used for exploratory analysis?

    <p>Data visualization (B)</p> Signup and view all the answers

    What is a key disadvantage of conducting a census?

    <p>It is time-consuming and expensive. (C)</p> Signup and view all the answers

    Which type of population is usually difficult to measure in research?

    <p>Dynamic populations (B)</p> Signup and view all the answers

    What type of data has natural ordering and can be grouped into bins?

    <p>Ordinal data (D)</p> Signup and view all the answers

    Which of the following statements is true regarding representative sampling?

    <p>It ensures the sample reflects the target population. (B)</p> Signup and view all the answers

    Which term describes data that involves counting distinct values?

    <p>Discrete data (A)</p> Signup and view all the answers

    Which type of data is characterized by non-integer values?

    <p>Continuous data (C)</p> Signup and view all the answers

    Study Notes

    Announcements

    • WebAssign sign-up due today
    • Homework 1 due Friday
    • Download Excel file
    • Review Canvas page
    • Note important dates from syllabus

    Data Basics

    • Variables can be numerical (numbers) or categorical (groups or bins)
    • Numerical variables can be discrete (e.g., 1, 2, 3) or continuous (e.g., decimal places)
    • Categorical data can be ordinal (e.g., natural ordering) or nominal (e.g., no natural ordering)
    • Explanatory variables might affect the response variable
    • Observational studies don't interfere with data, while experiments randomly assign subjects to treatments

    Review Problem

    • Problem A: Identify explanatory and response variables in a study of social media use and marriage satisfaction. Determine if it is an observational study or an experiment.
    • Reasoning: Explain why one approach is more suited than the other, considering if researchers intervene or observe

    Variable Type Example

    • Problem: Categorize "Cooking Experience" (rated 1-5).
    • Categorization: Categorical (1, 2, 3, 4, 5), Ordinal (ordinal scale)

    Chapter 1.3

    • Topic: Observational studies, sampling strategies

    Sample Definition

    • Research Question: Average fruit/vegetable consumption of Duke undergraduates
    • Sample: 200 Duke Undergraduates
    • Target Population: All currently enrolled Duke undergraduates
    • Purpose of sampling: Estimate the average for the target population

    Census

    • A survey that includes every member in the entire population
    • Census is time consuming and expensive
    • Populations constantly change, making it difficult to keep track of
    • Some characteristics may be hard to measure

    Exploratory Analysis to Inference

    • Sampling: Examining a small part of something (e.g., a spoonful of soup) to understand the entire thing.
    • Exploratory analysis: Deciding the spoonful isn't salty enough
    • Inference: Conclude that the whole soup needs salt.
    • Representative Sample: A sample which accurately reflects the population.

    Sampling Bias

    • Non-response bias Subjects chosen may not participate (e.g., email survey to students, few reply)
    • Voluntary response bias Participants with strong opinions may be overrepresented
    • Convenience sample bias Subjects most easily accessible tend to be disproportionately included

    Sampling Bias Example - Landon vs. FDR

    • 1936 Presidential election.
    • Magazine (Literary Digest) made predictions with a biased sample (readers, auto owners, phone users).
    • Prediction was wrong, magazine was discredited.

    The Literary Digest Poll

    • Poll: Surveyed about 10 million Americans, receiving responses from approximately 2.4 million
    • Results: Predicted Landon as an overwhelming winner, with FDR only receiving 43% of the votes
    • Reality: FDR won with 62% of the votes

    Literary Digest Poll - What Went Wrong?

    • Surveyed: The magazine's own readers, automobile owners, and telephone users
    • Issues: These groups tend to be higher income and lean Republican
    • Negative Impact: The poll's results significantly misrepresented the actual population

    Survey on Parking Policy

    • Issue: School district considering a change to high school student parking policy.
    • Survey: Sent out 6000 surveys by mail to parents
    • Response: 1200 completed
    • Results: 960 agreed with change, 240 disagreed.
    • Evaluations:
      • Some mailings might not have reached parents
      • Strong support for the change
      • Possible majority of parents could disagree with change
    • Potential Bias: Not all parents responded.

    Obtaining Good Samples

    • Almost all statistical methods are based on implied randomness.
    • Observational data not randomly gathered may not give reliable estimates or errors.

    Types of Samples

    • Simple Random Sample (SRS): Individual cases randomly selected from the population without any connection between them.
    • Stratified Sample: Population divided into subgroups (strata) and SRS is used on each group.
    • Cluster Sample: Population grouped into clusters. SRS is used to select clusters, then observations in these clusters.
    • Multistage Sample: Combination of sampling techniques like SRS and cluster sampling

    Practice Problem - Household Survey

    • Context: Survey in suburban area, diverse neighborhoods (large homes, apartments).
    • Least Effective Approach: Cluster sampling.

    Practice Problem - Cats on YouTube

    • Context: Estimating percentage of cat videos on YouTube.
    • Sample: 1000 videos randomly selected
    • Observation: Each video selected
    • Variable: Whether or not a video is a cat video
    • Sample Statistic The percentage (2%) of videos in the sample that were cat videos
    • Population Parameter: The proportion of all videos on YouTube that are cat videos.

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    Related Documents

    Chapters 1.2-1.3 Class Notes PDF

    Description

    Test your understanding of data basics, including the types of variables and research methods. This quiz covers numerical and categorical variables, as well as observational studies and experimental designs. Analyze the problems presented and apply your knowledge.

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