Expanded Concepts for PSYC 2317 - Comprehensive Final Exam Review

Chapter 1

• Population

  • Definition: The entire group of interest to a researcher.

  • Example: Study of smoking habits among students at Houston Community College encompasses all students at that institution.

• Sampling Error

  • Definition: The natural discrepancy between a sample and the corresponding population.

  • Insight: Populations can contain thousands to millions of individuals while samples typically consist of around 30, leading to discrepancies.

• Scales of Measurement

  • Nominal:

    • Definition: Simple named categories with no implied order.

    • Examples: Favorite color, academic major, city of residence.

  • Ordinal:

    • Definition: Named categories with ranks.

    • Examples: Placement in a race, Olympic medals (gold, silver, bronze).

  • Interval:

    • Definition: Numeric measurements where zero is not an absence.

    • Examples: Temperature on Celsius scale; 0 degrees denotes freezing but does not imply absence of temperature.

  • Ratio:

    • Definition: Numeric measurements where zero represents absence.

    • Examples: A response of "0" pets indicates no pets owned.

• Sum Calculations

  • Sum of X ( ∑𝑋) and Sum of X Squared ( ΣX²) are covered in the Computation Section of the PSYC 2317 Comprehensive Final Exam Review.

Chapter 2

• Sum of X from Frequency Distribution Table

  • This calculation is also covered in the Computation Section.

• Distribution Shapes

  • Normal: Refer to Lecture Notes in Chapter 2 presentation, page 36.

  • Negatively Skewed: See page 39 of the same presentation.

  • Symmetrical: Refer to page 39.

• Frequency Distribution Analysis

  • Determining the shape of a distribution.

  • Identifying the total number of individuals represented.

Chapter 3

• Calculating Mean

  • Definition: The mean is identical for both populations and samples and is discussed in the Computation Section.

• Calculating Weighted Mean

  • Covered in the Computation Section.

• Median Calculation

  • Necessary for understanding data distribution.

• Mode Calculation

  • Important for analyzing frequency distribution tables.

• Mean, Median, and Mode Locations in Different Distributions

  • Normal: Refer to Lecture Notes, page 34.

  • Negatively Skewed: Refer to page 34.

  • Positively Skewed: Refer to page 34.

Chapter 4

• Sum of Squared Deviations (SS)

  • Definition and calculation protocol are the same for populations and samples.

• Population Variance Calculation

  • Based on provided population standard deviation.

• Sample Variance Calculation

  • Process requires sum of squares and sample size (n).

Chapter 5

• Z-Score Interpretation

  • Need to understand how to interpret and calculate z-scores, which are covered in the Computation Section.

• Comparing Distributions with Z-Scores

  • Essential for analyzing different data sets based on z-scores.

Chapter 6

• Requirements for Random Sampling

  1. Sampling with replacement is necessary.

  2. Probabilities must remain constant during selections.

  3. Equal chance of selection for every individual to preserve fairness.

• Importance of Sampling with Replacement

  • Example with raffle tickets illustrating effect on probabilities when sampling without replacement.

• Finding Probability of X Value/Z-Score

  • Requires using the Unit Normal Table, best learned with visual aid.

Chapter 7

• Expected Value of M (𝜇𝜇𝜇𝜇)

  • Represents the mean of sampling distribution, equivalent to the population mean.

• Standard Error of M (𝜎𝜎𝑀𝑀)

  • Represents average discrepancy between sample means and population mean, serving as a measure of sampling error.

• Standard Error Calculation

  • Discussed in the Computation Section.

• Z-Score of Sample Mean Calculation

  • Covered in the Computation Section.

Chapter 8

• Null Hypothesis (H0)

  • Introduction: Proclaims no treatment effect or relationship between variables.

• Alternative Hypothesis (H1)

  • Asserts that there is a treatment effect or relationship among variables.

• Type I Error

  • Occurs when rejecting the null hypothesis when it is true.

• Relationship Between Alpha Level and Type I Error

  • Higher alpha levels lead to larger critical regions, increasing Type I error risks.

• Statistical Decisions

  • Involves determining conclusion from critical boundaries and sample data result.

• One-Tailed vs. Two-Tailed Tests

  • Easier to reject null for one-tailed tests aligned with researcher’s hypothesis.

Chapter 9

• Sample Size, Variance, and Estimated Standard Error

  • The calculation demonstrates that larger sample size reduces estimated standard error while larger sample variance increases it.

• Estimated Standard Error Calculation

  • Discussed in the Computation Section.

• Finding Critical Boundaries for Single-Sample T-Test

  • Involves comprehension of alpha levels and participants involved.

Chapter 10

• Independent-Measures Design

  • Examples: Two participant groups compared for satisfaction with keyboard designs using an independent-measures t-test.

Chapters 12 & 13 (ANOVAs)

• F-Ratio Calculation

  • Derived from MS between and MS within, crucial for analysis.

• Statistical Decisions from ANOVA

  • Decisions based on result, degrees of freedom, and alpha level.

Chapter 14

• Correlation Coefficient Calculation

  • Measure relationship strength between two variables.

Chapter 15

• Expected Frequency Calculation

  • Method for determining expected outcome under the null hypothesis.

• Chi-Square Goodness-of-Fit Test

  • Comparison metrics within the test and their implications.

• Chi-Square Goodness-of-Fit Statistic Calculation

  • Understanding calculation protocols for accurate statistical testing.