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; $(\bar{x} = \frac{\sum<em>{i=1}^{n} x</em>i}{n})$.
  • 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; $(\sigma = \sqrt{\frac{\sum<em>{i=1}^{n} (x</em>i - \mu)^2}{n}})$, 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; $(\sigma^2 = \frac{\sum<em>{i=1}^{n} (x</em>i - \mu)^2}{n})$.
• 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.