Key Marketing Research Variables and Editing

Posted on Mar 10, 2025 in Mathematics

Scales of Measurement in Marketing Research

Understanding different scales is crucial for accurate data interpretation.

  • Nominal Scale: Numbers serve only as labels or tags for identifying and classifying objects. In marketing research, it’s used to identify respondents, brands, attributes, and other objects. Example: Numbers assigned to runners in a race.
  • Ordinal Scale: Indicates rank order, providing directional information in addition to nominal information. It measures non-numeric concepts like satisfaction levels. Example: Ranking customer satisfaction from least to most satisfied.
  • Interval Scale: Possesses equal intervals between values, providing information about the distance between objects. Example: Performance rating on a scale of 0-10, or Celsius temperature.
  • Ratio Scale: Has an absolute zero point, indicating the absence of the quality being measured. Example: Time to finish a race in seconds, or the amount of money in a pocket.

Types of Variables in Marketing Research

Variables play different roles in understanding cause-and-effect relationships.

  • Independent Variable: The variable that is manipulated in an experiment to observe its effect on a dependent variable. Also called an experimental or predictor variable.
  • Dependent Variable: The variable that is measured in an experiment and is dependent on the independent variable(s). Also called an outcome variable. Example: If a scientist tests if a vitamin extends life expectancy, the independent variable is the vitamin amount, and the dependent variable is life expectancy.
  • Intervening Variable: Explains the relationship between the independent and dependent variables when a direct connection isn’t obvious. Example: Education (independent) leads to higher income (dependent). Occupation is an intervening variable, affected by education and affecting income.
  • Moderating Variable: Alters the impact of an independent variable on a dependent variable. It can strengthen, weaken, or change the direction of the relationship. Moderators indicate *when* or *under what conditions* an effect is expected.

Data Editing in Marketing Research

Ensuring data accuracy and reliability is a two-stage process.

  • Field Editing

    A field supervisor’s responsibility, performed soon after data collection. It involves reviewing data collection forms for completeness and legibility. Callbacks may be necessary to clarify missing or ambiguous entries. Typically, 10% of data is validated.

  • Central Editing

    A thorough review of the data, ideally by a single editor for consistency (in smaller studies). Editors may correct obvious errors by reviewing other data. This phase also helps detect fake interviews (“armchair interviewing”), especially in open-ended questions.