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Exam 1 Study Guide: Chapters 1 through 4 - Coggle Diagram
Exam 1 Study Guide: Chapters 1 through 4
Course Structure
Quizzes (14%)
6 Quizzes, best 4 grades count
Top Hat (12%)
17 total, top 12 used for grading
Research Engagement (5%)
Participate in research
Exams (69%
Exam 1: 20%, Exam 2: 23% Exam 3: 26%
Scientific Method & Statistics
Theory
A principle explaining relationships between variables
Based on prior knowledge and research
Hypothesis
Educated guess about the relationship between variables
Hypotheses are testable and based on theories
Hypothesis Testing
Setting a Hypothesis
Define the variables and operationalize them
Must be testable, often phrased as "if the theory is true, then...."
Sources of Error:
Operational Definition
: Ensures accurate measurement of abstract concepts.
Confounds
: Uncontrolled variables that distort the relationship between IV and DV.
Sampling Error
: If the sample does not represent the population, results can be misleading.
Descriptive Statistics
Used to summarize and organize data
Focus on patterns, averages (mean, median and mode), and visual representations (graphs)
Inferential Statistics
Used to make predictions or generalizations about populations from sample data
Helps determine whether observed patterns are due to chance or meaningful relationships
Classifying Variables
Discrete Variables
Specific values, no intermediates (e.g. cookie types, military ranks)
Examples: Nominal (categories), Ordinal (ranked categories with unequal intervals)
Continuous Variables
Can take any value within a range (e.g. temperature, reaction time)
Examples: Interval (ranked, no true zero), Ratio (ranked with true zero)
Scales of Measurement
Nominal
Categories without ranking (e.g. gender, colours)
Ordinal
Categories with ranking, but not equal intervals (e.g. rankings in a race)
Interval
Ordered with equal intervals, but no absolute zero (e.g. temperature in celsius)
Ratio
Ordered with equal intervals and true zero (e.g. height, weight)
Frequency Distributions
Summarizes how often each value of a variable occurs in a data set
Proportion Frequency
: Fraction of observations for each value (calculated as f/n)
Frequency Percent
: Proportion expressed as a percentage (f/n) x100)
Grouped Frequency Distributions:
Useful for large data sets with wide ranges
Class Interval: Defines the range of values for each group (e.g., income brackets).
Rules for Distributions
Aim for 5-15 groups with convenient interval sizes (e.g., 10s, 25s)
Lower limits should be even multiples of the interval size
Research Methods
Independent Variable (IV)
The variable manipulated or controlled by the researcher (e.g. type of treatment in an experiment)
Dependent Variable (DV)
The outcome or response measured to determine the effect of the IV (e.g. test scores after a learning method)
Study Designs
Correlation
Examines the association between two or more variables (no manipulation)
Experiment
Manipulates the IV to see the effect on the DV (cause and effect)
Quasi-Experiment
Groups compared without random assignment (e.g. natural groups)
Measures of Central Tendency
Mean
Sum of all values divided by the number of values
Preferred when data is symmetrically distributed
Median
The middle value when data is ordered from lowest to highest
Useful for skewed distributions (less affected by outliers)
Used for skewed data or with outliers
Mode
The most frequent value in the data set
Common in categorical data when frequency of a specific category is important
Best for nominal data
Measures of Variability
Range
Difference between highest and lowest values in the data set
Sensitive to outliers
Variance
Measures how far each value in the data set is from the mean
Standard Deviation (SD)
The square root of the variance; represents the typical distance between scores and the mean
Larger SD indicates more variability in the data set
Sampling
Population
The entire group of individuals or events of interest
Example: All students at a university
Sample
A smaller, representative subset of the population
Used to make inferences about the population
Types of Sampling
Random Sampling
Every individual has an equal chance of being selected
Convenience Sampling
Selection is based on accessibility (not ideal for generalizing results)
Inferential Statistics
Goal:
To determine if observed differences in the dependent variable (DV) reflect true differences in the population
Helps make predictions or test hypotheses
Challenge:
High variability in data can make it harder to detect meaningful patterns
Larger sample sizes or reduced variability help improve the reliability of inferences.