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• Pierce's Four Paths of Establishing Knowledge, ranked from weakest to strongest, include:
  • Method of Tenacity: Holding onto familiar beliefs for comfort and peace of mind
  • Method of Authority: Accepting something as true because an authority figure says so
  • A Priori Method: Relying on logical reasoning without direct observation
  • Scientific Method: The most reliable method, using systematic observation, experimentation, and data analysis

Dialectics in Research (Contrasting Research Approaches)

• Basic vs. Applied
  • Basic Research: Expands knowledge, tests theories.
  • Applied Research: Solves real-world problems, informs decisions.
• Lab vs. Field
  • Lab Research: Controlled settings, better precision.
  • Field Research: Real-world settings, more realistic findings.
• Inductive vs. Deductive Reasoning
  • Deductive (Top-Down): Start with a hypothesis, collect data, and see if data supports or rejects the hypothesis
  • Inductive (Bottom-Up): Start with data collection, identify patterns, and formulate a conclusion which can be tested deductively

Qualitative vs. Quantitative Research

• Qualitative Research (Exploratory, In-Depth)
  • Uses open-ended questions and in-depth interviews
  • Employs focus groups
  • Can help generate background information and is useful in problem formation
  • Seeks input from individuals (e.g., consumers)
• Quantitative Research (Statistical, Measurable)
  • Best for generalizing results
  • Identifies differences between groups, and uses statistical analysis
  • Data collection methods include observations, surveys, and experiments

Measurement & Conceptualization

• Measurement: Links abstract concepts to real-world observations, following rules for assigning numbers to represent attributes.
• Ways to Measure a Concept (Example: Reading)
  • Includes self-report (e.g., survey asking how often someone reads)
  • Involves observation (e.g., tracking time spent reading)
  • Existing Data (e.g., using library records, secondary data)
• Concepts in Research: A generalized idea about a class of objects, attributes, or processes.
  • Examples of Advertising-Related Concepts: Advertising efficacy, brand equity, product use, and brand loyalty.

Conceptual Definition vs. Operational Definition

• Conceptual Definition:
  • A verbal explanation of a concept, which defines what it is and is not.
  • Example: In advertising research, "attitude" is a person's evaluative judgment of a product, ad, or brand.
• Operational Definition:
  • Specifies how the concept is measured, ensuring it is observable and testable.
  • Example: Measuring "attitude" through survey responses on a Likert scale (1 = very unfavorable, 5 = very favorable).
• Operationalism: Ensures scientific concepts are grounded in observable, measurable events, eliminating subjectivity and allowing others to replicate the measurement process.
• Steps to Develop an Operational Definition: Specify the concept of interest, identify different aspects of the concept's meaning, and explicitly state observable indicators
• Select the best method(s) of measurement
• Conceptualization: Defining a variable (e.g., what does "intelligence" mean?)
• Operationalization: How you measure the variable (e.g., IQ test scores).

Descriptive Statistics

• Descriptive statistics summarize data
• Measures of Central Tendency (Find the "center" of data)
  • Mean: The average.
  • Median: The middle score.
  • Mode: The most frequent score.
  • Application: Income distributions often have a positive skew, meaning the median is a better measure than the mean.
• Measures of Variability (Spread of data)
  • Range: Maximum score - Minimum score.
  • Standard Deviation (SD): Indicates how much scores vary from the mean.
  • Variance: Represents the squared differences from the mean.
  • Z-Score: Shows how many standard deviations a score is from the mean.
    • Formula: Z = (Χ - μ) / σ
    • Interpretation: Helps compare scores across different distributions.
• Frequency Distributions
  • Used to visualize data distribution
  • Types:
    • Frequency polygons (e.g., tracking sardine sizes over 16 seasons)
    • Histograms (used to check skewness)
    • Normal distribution curves
• Probability Distributions
  • Normal Distribution: Bell-shaped curve where the mean = median = mode
  • Skewed Distributions:
    • Positive Skew: Tail on the right (e.g., income data)
    • Negative Skew: Tail on the left
• Measures of Dispersion (Spread)
  • Deviation Scores: Differences between each observation and the mean
  • Mean Squared Deviation: Squaring each deviation and averaging.
  • Formulas:
    • Population Mean (μ): ΣΧ / N
    • Sample Mean (X): ΣΧ / n
    • Variance (σ² or s²): Σ(X - X)² / (n-1)

Ethical Considerations in Research

• Ensuring validity & reliability of data, avoiding misleading correlations, and reporting results transparently
• Conceptualization: Defining a variable (e.g., "Age is the number of years a person has lived")
• Operationalization: Measuring a variable in a study (e.g., "How old are you?") with a textbox entry for numerical input
• Descriptive Statistics
  • Mean: The average value
  • Median: The middle score when data is ordered
  • Mode: The most frequently occurring value
• Variability (Spread of Data)
  • Range: Max score - Min score (Difference between highest & lowest values)
  • Standard Deviation (SD): Measures how much scores vary from each other
  • Z-Score: Shows how far a single score is from the mean in standard deviation units

Normal Distribution & Standardization

• Normal Distribution (Bell Curve)
  • Most values fall within ±3 SDs
  • IQ scores are an example of normally distributed data
• Standardized Normal Distribution
  • Mean = 0, SD = 1
  • Used to compare different distributions
• Deontological Ethics:
  • Actions are inherently right or wrong
  • Rights Principle:
    • Universality (applies to everyone)
    • Reversibility (Golden Rule: treat others as you'd want to be treated)
  • Justice Principle:
    • Distributive: Fair allocation
    • Retributive: Punishment should fit the wrongdoing
    • Compensatory: Harm should be restored (e.g., insurance)
• Teleological Ethics (Utilitarianism):
  • Focuses on the greatest good for the greatest number
  • Ethical evaluation is based on consequences
  • Key questions:
    • What are the harms and benefits?
    • What's the least harm I can do?

Research Ethics Guidelines

• Respect for Persons: Informed consent & protection of vulnerable individuals
• Beneficence: "Do no harm" while maximizing research benefits
• Justice: Fair distribution of benefits and burdens
  • Other Considerations:
    • Voluntary participation
    • No harm to participants
    • Anonymity & confidentiality
    • Informed consent
    • Minimizing deception
• Levels of Measurement
  • Nominal: Categories (e.g., gender, race) – Only mode can be calculated
  • Ordinal: Ranked order (e.g., satisfaction ratings) – Median & mode
  • Interval: Equal intervals, no true zero (e.g., temperature) – Mean, median, mode
  • Ratio: Absolute zero exists (e.g., weight, income) – Allows full statistical analysis
• Always collect data at the highest level possible for better analysis
• Statistics & Variability
  • Variance: Measures how far each number in a dataset is from the mean (squared units)
  • Standard Deviation (SD): Square root of variance; measures average deviation from the mean
    • Sample Variance Formula: S2=∑(X-X¯)2n-1S^2 = \frac{\sum (X - \bar{X})^2}{n-1}S2=n-1∑(X-X¯)2
  • Sample Standard Deviation Formula: S=∑(X-X¯)2n−1S = \sqrt{\frac{\sum (X - \bar{X})^2}{n-1}}S=n-1∑(X-X¯)2
• Normal Distribution
  • Bell Curve: Most values fall within ±3 SDs
  • Standardized Normal Distribution:
    • Mean = 0, SD = 1
    • Symmetrical & continuous

Ethics in Research

• Total area under curve = 1.0
• Z-Scores: Standardized values calculated as Z=X-μσΖ = \frac{X - \mu}{\sigma}Z=σΧ-μ
• Why Standardization Matters:
  • Makes different scores comparable
  • Helps determine probabilities in a normal distribution
  • Useful for central limit theorem applications
• Ethics: Principles guiding right vs. wrong in research
  • Deontology (Duty-Based Ethics):
    • Actions are inherently right or wrong
    • Rights Principle:
      • Universality: If an action is right for one, it should be right for all
      • Reversibility: Golden rule – treat others as you'd like to be treated
    • Justice Principle:
      • Distributive: Fair allocation of resources
      • Retributive: Punishment should fit the wrongdoing
      • Compensatory: Those harmed should be restored
  • Teleology (Consequence-Based Ethics):
    • Utilitarianism: Maximizing good for the greatest number
    • Questions to consider:
      • What are the harms and benefits?
      • How can harm be minimized?
• Code of Ethics in Research
  • Respect for Persons:
    • Informed consent
    • Protection of vulnerable individuals
  • Beneficence:
    • Minimize harm
    • Maximize benefits
  • Justice:
    • Fair distribution of research benefits and burdens
• Other Ethical Issues:
  • Voluntary participation
  • No harm to participants
  • Anonymity & confidentiality
  • Informed consent
  • Minimized deception
• Research Process
  • Research Question: A question guiding the study
  • Hypothesis: A predictive statement tested through research
  • Scientific Method: Research is necessary to verify or refute beliefs
• Levels of Measurement
  • Nominal: Labels/categories (e.g., gender, race) – Mode only
  • Ordinal: Ordered categories (e.g., rankings) - Median & mode
  • Interval: Equal intervals, but no true zero (e.g., temperature) – Mean, median, mode
  • Ratio: True zero exists (e.g., weight, income) – Full range of statistical operations