Chap007_PPT_Accessible (1)

Learning and Measurement in Marketing Research

Importance of Measurement

• Measurement is crucial in modern marketing research as it allows marketers to quantify and understand abstract concepts such as consumer preferences, brand loyalty, and market trends.

• Accurate measurement directly influences effective decision-making, guiding strategic marketing initiatives by providing insights into customer behaviors and attitudes.

• Effective measurement not only helps in identifying current market conditions but also in forecasting future trends based on the collected data.

Types of Data

Structured Data

• Structured data is organized in a defined format, typically stored in relational databases or spreadsheets, making it easily accessible and analyzable.

• This type of data is often numerical and referred to as quantitative data, allowing for statistical analysis and trend identification.

• Examples include sales figures, customer demographics, and web analytics, which are frequently utilized for immediate decision-making and performance tracking.

Unstructured Data

• Unstructured data lacks a predefined format and includes formats like videos, audio recordings, text documents, and social media posts.

• This data is classified as qualitative data and requires transformation into structured formats through techniques such as coding or data mining for effective analysis.

• Unstructured data offers richer, more nuanced insights but poses challenges in analysis due to its complexity.

Measurement Process Overview

Measurement Tasks

1. Construct Selection/Development: Identify and define what phenomena or attributes are being measured—such as customer satisfaction or brand perception.

2. Scale Measurement: Determine appropriate measurement methods for constructs, ensuring precision and reliability in the data collected.

Constructs

• Definition: Constructs represent abstract ideas formed through measurable characteristics that can be inferred indirectly.

• For example, measuring customer interaction can utilize scales that cover a range of agreement levels, from completely disagree to completely agree.

• Construct Development involves:

  • Starting with a clear study purpose and research problem.

  • Identifying key characteristics to define and measure the concept effectively.

Construct Dimensionality and Validity

• Qualitative Data: This type of data is employed for gaining insights into unobservable characteristics that cannot be easily quantified.

• Types of Constructs:

  • Unidimensional: Measured as a single factor, focusing on one specific attribute.

  • Multidimensional: Comprised of several interconnected components, offering a more holistic view of the concept being studied.

• Validity Measures include various forms of validity assessments: content validity (whether the measurement covers the concept), convergent validity (whether it correlates well with similar constructs), discriminant validity (differentiating from unrelated constructs), and nomological validity (how well it fits into existing theoretical frameworks).

Categorical Types of Information

• State-of-Being: Physical, demographic, or socioeconomic characteristics that provide background on a subject or population.

• State-of-Mind: Mental attributes or emotions regarding an object, though this may be limited by respondents' honesty and self-awareness.

• State-of-Behavior: Current and prior actions observable through surveys or records, critical for understanding customer actions.

• State-of-Intention: Refers to individuals' plans for future behaviors, which can be forecasted through behavioral intention scales.

Properties of Measurement Scales

Scaling Properties:

• Assignment Property: Provides unique descriptors that allow for the identification of different objects or categories in the data.

• Order Property: Establishes relationships among objects based on their relative magnitudes, important in understanding preferences.

• Distance Property: Identifies the absolute differences in a measurable format, allowing for rigorous comparisons between items.

• Origin Property: Establishes a true zero point in measurement representing a complete absence of the quantity being measured, which is vital in ratio scales.

Scale Measurement

• Scale Descriptors: Labels and associated numbers correspond to responses, where intensities are classified as scale points.

Levels of Scale:

1. Nominal: Represents discrete categories without any inherent order, suitable for distinguishing different groups.

2. Ordinal: Represents ordered categories that indicate relative preferences, but without specifying the distance between them.

3. Interval: Indicates absolute differences between values but does not have a true zero point, useful in measuring attributes like temperature.

4. Ratio: Features a true zero, allowing for meaningful ratios and comparisons—essential for measurements in marketing such as revenue.

Evaluating Measurement Scales

Reliability:

• Refers to the consistency in measurement outcomes across various trials, with methods including test-retest reliability and equivalent form techniques being central in assessing reliability.

Validity:

• Evaluates whether a scale accurately measures what it is intended to measure, including aspects of face validity (apparent adequacy on the surface) and content validity (completeness of the measure).

Scale Development Criteria

• Key considerations include understanding the research problem and the specific objectives of the information desired.

• Utilization of pretests is critical to ensure comprehensibility and clarity for respondents.

• It is paramount to establish a balanced approach in scale descriptors to facilitate unbiased responses from participants providing data.

Types of Measurement Scales

• Likert-Type Scale: Measures levels of agreement with specific statements related to attitudes or behaviors, often on a range of 1-5 or 1-7.

• Semantic Differential Scale: Captures attitudes using bipolar descriptors anchored at either end, allowing respondents to express varying degrees of opinion.

• Behavioral Intention Scale: A scale that aims to predict future purchasing behavior based on stated intentions.

• Comparative Rating Scales: Involve participants ranking preferences using methods like rank-order or constant sum, which provide insights into priority relative to alternatives.

Design Considerations

• Questions must be clear and concise, avoiding jargon or complex terminology.

• Care should be taken to avoid misleading formats, such as double-barreled questions that can confuse respondents.

• Scale responses need to be mutually exclusive (no overlap in response choices) and exhaustive (covering all possible response options to avoid ambiguity).