PSYC 299 Exam 1 Study Notes

Review Topics for PSYC 299 Exam 1

  • Exam Format

    • Mixture of multiple choice, short answer, and computations.

    • Tools Allowed: Bring a calculator (not a phone/computer/tablet app).

    • Resources Not Allowed: You cannot use books, class slides, or other resources.

    • Location: Meet in the same room and at the same time as regular class.

  • Exam Tips

    • Double check all answers.

    • Show all work: To receive full credit, you must demonstrate your calculations, especially for deviation scores (e.g., example given must show work).

    • Pay attention to question scoring: Be mindful of how much each question is worth and allocate your time accordingly.

Introduction & Variables and their Measurement (Modules 1 and 2)

  • Basic Concepts

    • Populations vs. Samples: Understand the basic distinctions between these two concepts.

    • Types of Statistics: Recognize the differences between two types of statistics:

    • Descriptive Statistics: Summary metrics that describe the main features of a data set.

    • Inferential Statistics: Procedures that allow us to infer or generalize from a sample to a population.

    • Independent and Dependent Variables: Identify and differentiate between these two fundamental variables in research.

    • Experimental vs. Correlational Designs: Understand the basic differences between experimental and correlational research methodologies.

    • Operational Definitions: Know what constitutes an operational definition and the process of creating one.

  • Scale of Measurement (SoM)

    • Ability to identify different measures based on the four properties discussed in class:

    • Differentiation: Distinction between different types.

    • Ordering by Differentiation: Rank order on a meaningful continuum.

    • Equal Intervals: Fixed degree of change corresponds to fixed degree of change in measurement.

    • Absolute Zero: Represents the total absence of the attribute being measured.

  • Types of Scales

    • Nominal Scale:

    • Only differentiation with cases classified as types.

    • Example: Brands of jogging shoes, kinds of fruit, types of music.

    • Ordinal Scale:

    • Incorporates differentiation and ordering by differentiation, allowing for rank order.

    • Example: Olympic medalists, favorite sports.

    • Interval Scale:

    • Has differentiation, ordering by differentiation, and equal intervals, but does not have an absolute zero.

    • Example: Temperature in Fahrenheit or Celsius, time of day, day of the month.

    • Ratio Scale:

    • Incorporates differentiation, ordering by differentiation, equal intervals, and features an absolute zero.

    • Example: Metric or imperial scales of length, weight, or speed.

Data Organization (Module 3)

  • Interpreting Graphs

    • Focus on understanding histograms: Identify features such as center, spread, shape (skewness), and outliers.

  • Types of Graphs

    • Know when to use certain graph types: Pie charts, bar graphs, histograms, or line graphs.

  • Frequency Distribution Table

    • Ability to read and create a frequency distribution table, along with graphs based on it (e.g., bar or histogram).

Central Tendency (Module 4)

  • Measures of Central Tendency

    • Know how to compute the three measures:

    • Mean: The average of a data set.

    • Median: The middle value in a data set when ordered.

    • Mode: The value that appears most frequently in a data set.

  • Understanding Differences

    • Understand how these measures differ and relate to each other, especially in the context of skewed vs. normal data distributions.

  • Usage of Each Measure

    • Know when each measure of central tendency is most appropriate to use.

Measures of Variation and Distribution Shape (Module 5)

  • Measures of Variability

    • Compute key measures of variability:

    • Range: Difference between the highest and lowest values in a data set.

    • Variance: Measure of the data's dispersion around the mean.

    • Standard Deviation: Indicates how much scores in a data set differ from the mean, calculated using the formula:
      SD=VarianceSD = \sqrt{Variance}

  • Pros and Cons

    • Understand the advantages and disadvantages of using range versus standard deviation as measures of variability.

  • Thinking About Standard Deviation

    • Four perspectives on standard deviation:

    1. Average of Differences: Indicates how much each case differs from the mean (μ or M).

    2. Random Selection: How much a randomly selected case differs from the mean.

    3. Inter-case Differences: How far all scores differ from each other.

    4. Case-to-Case Differences: How far any randomly selected case is from another case.

  • Impact of Skewness

    • Understand how skewness affects the three measures of central tendency: e.g.,

    • In a normal distribution: mean = median = mode.

    • In a skewed distribution: mean and median shift toward the tail, with mean being the most affected.

  • SPSS Usage

    • Ability to request appropriate measures in SPSS and know how to copy and paste results.

Probability (Module 7)

  • Probability Computation

    • Understand how to compute probability based on outcomes and events.

  • Interpretation

    • Be able to explain what probability means in everyday language, connecting theoretical understanding to practical implication.