PLSC 3603-004 - Button: Study Guide, Spring 2022 Final Exam

Exam Overview

  • The Scope and Methods final exam is scheduled for 5/11/22 from 12:45 pm to 2:45 pm.
  • The study guide is meant to supplement ongoing studying and is not a comprehensive overview.
  • The exam will primarily include multiple choice and true/false questions. No calculations are required.
  • There will be two essay-style two-paragraph response questions.

Chapter 9

  • Cohort group: Understand what defines a cohort and why this grouping is significant in research.
  • Survey design unit of analysis: Focus on how the unit of analysis impacts the survey design and the interpretation of results.
  • Types of randomized and non-randomized experiment designs:
    • Characteristics of each design and how to identify them.
    • Understand the differences between randomized controlled trials, quasi-experiments, and observational studies.
  • Types of observational studies:
    • Characteristics of different observational study designs (e.g., case-control, cohort).
    • Distinguish between experimental and observational studies.

Chapter 10

  • Creating a quality survey: Understand considerations for crafting effective and valid surveys.
  • Types of questions: Knowing the types of questions (open-ended, closed-ended, Likert scale, etc.) and identifying good versus bad questions.
    • Understand the impact of question wording and ordering on responses.
  • Levels of populations: Understanding the different levels of populations as they pertain to the data and validity.
  • Content analysis:
    • Core processes: Focus on the steps involved in content analysis (e.g., coding, categorization).
    • Types of data: Focus on the different types of data used in content analysis (e.g., text, images, audio, video).
    • Data processing terminology: Understand key terms used in content analysis.

Chapter 11

  • Data and statistical analyses: Emphasizing understanding of matrix algebra and statistical theory.
  • Frequency distributions:
    • Types of frequency distributions (e.g., histograms, frequency tables).
  • Measures of central tendency:
    • Mean: The average of a dataset, calculated as the sum of all values divided by the number of values; (\bar{x} = \frac{\sum{i=1}^{n} xi}{n}).
    • Median: The middle value in a dataset when ordered from least to greatest; if there is an even number of observations, the median is the average of the two middle values.
    • Mode: The value that appears most frequently in a dataset.
  • Central Limit Theorem: Understanding how the distribution of sample means approximates a normal distribution as the sample size increases, regardless of the population's distribution.
  • Normal distribution curve:
    • Characteristics: Know the properties of the normal distribution (e.g., symmetric, bell-shaped).
  • Skewness: Understanding positive and negative skew and how it affects the mean, median, and mode.
  • Statistical inferences: Understanding how analytics and interpretations of the distribution help in making statistical inferences about relevant hypotheses.
  • Confidence intervals:
    • CI = \bar{x} \pm z*(\frac{\sigma}{\sqrt{n}}), where \bar{x} is the sample mean, z is the z-score, \sigma is the population standard deviation, and n is the sample size.
  • Influence of outliers: Understand how outliers can affect measures of central tendency and dispersion.
  • Dispersion:
    • Deviation from the mean: Measures of how spread out the data are from the average.
  • Descriptive statistics:
    • Range: The difference between the maximum and minimum values in a dataset.
    • Standard deviation: A measure of the amount of variation or dispersion in a set of values; (\sigma = \sqrt{\frac{\sum{i=1}^{n} (xi - \mu)^2}{n}}), where \mu is the population mean.
    • Min/max: Identifying the smallest and largest values in a dataset.
  • Charts to display data: Types like histograms, bar charts, pie charts, and scatter plots and their appropriate uses.
  • Types of measures: Refresh knowledge on nominal, ordinal, interval, and ratio scales.

Chapter 12

  • Normal distribution:
    • Z-score: Converting raw scores into z-scores to understand their position relative to the mean; z = \frac{x - \mu}{\sigma}.
    • Distribution modes: Identify modes in the context of normal distribution.
    • Applicability of standard deviation: Understanding how standard deviation relates to the spread of the normal distribution.
  • Confidence intervals:
    • More on confidence intervals: Reviewing the construction and interpretation of confidence intervals.
  • T-test:
    • Calculating a t-test: Understanding the formula and application of the t-test; t = \frac{\bar{x} - \mu}{\frac{s}{\sqrt{n}}}.
    • Degrees of freedom: df = n - 1 (sample size minus one); Refresher on sampling errors.
  • Hypotheses:
    • Types of hypotheses: Null and alternative hypotheses.
    • Type I and Type II errors: Understanding the risks of incorrectly rejecting or failing to reject the null hypothesis.
    • Level of significance and p-values: The significance level (alpha) is the probability of rejecting the null hypothesis when it is true; the p-value is the probability of obtaining results as extreme as, or more extreme than, the observed results, assuming the null hypothesis is true.
    • Critical values for hypothesis testing: Using critical values to determine whether to reject the null hypothesis.
    • Difference of means tests: Comparing the means of two groups to determine if they are significantly different.
  • Why these calculations matter.

Chapter 13

  • Types of relationships:
    • Direction (positive, negative, curvilinear).
  • Measures of association:
    • How they are applied to different types of measures (e.g., Pearson's r for interval/ratio data, Spearman's rho for ordinal data).
  • Interaction: The effect of one variable on another depends on the value of a third variable.
  • Variation and variance:
    • Variance: A measure of how spread out a set of data is; (\sigma^2 = \frac{\sum{i=1}^{n} (xi - \mu)^2}{n}).
  • Correlation coefficients:
    • Pearson’s r, Spearman’s rho, etc.
  • Chi-squared:
    • Chi-squared test: A statistical test used to examine the association between categorical variables; \chi^2 = \sum \frac{(O - E)^2}{E}.
  • Characteristics of the values for these statistical concepts: Understand the range and interpretation of different measures of association.

Chapter 14

  • Regression Analyses:
    • Types of regression analyses.
  • Line of best fit.
  • 10 assumptions of OLS regression.
  • Residuals: the difference between the observed and predicted values.
  • Residual sum of squares.
  • Types of measures that best fit with OLS.
  • Regression coefficients.
  • Pearson’s r: Measures the strength and direction of a linear relationship between two variables, ranging from -1 to 1.
  • R-squared: The proportion of variance in the dependent variable that is predictable from the independent variable(s).
  • Scatterplots.
  • Nonlinear models:
    • Maximum likelihood: A method for estimating the parameters of a statistical model.
    • Importance of probability.
    • Homoscedasticity and heteroscedasticity: Understand the difference and implications for regression analysis.