Measurement Scales in Marketing Research Essay Example
Measurement Scales
If a researcher is going to use statistics properly, it is crucial to consider the kind of data about which descriptive statistics are to be calculated or to which will be applied some kind of statistical test. Statistical analysis, even simple analysis as counting, requires that each characteristic to be studied be assigned a unique value. Sometimes, especially in qualitative research with small samples, this value can be a word or symbol. For example, the interviewers could assign the word yes or positive to the symbol “+” to indicate that a respondent liked a product, flavor, or company. For a computer to prepare summary statistics or conduct a statistically analysis, each measurement of the sample population must be assigned a number. There are four categories which numbers are generally grouped. In increasing order of sophistication, they are (1) nominal numbers, (2) ordinal numbers, (3) intervally-scaled numbers, and (4) ratio-scaled numbers. These numbers are called data and are classified through scales.
Marketing research data is classified either metric or non-metric. Metric data are measured data, such as income, age, years or schooling. When the data are metric it can be thought to terms of average: What is the average income? Average age? Average years of school? Non-metric data on the other hand, are counted data. Gender, place of residence, and level of education are all non-metric data. When the data are non-metric we think in terms of percent-ages: What proportion of Kentwood residents are male? What percentage of the population is Hispanic? What percentage of Davenport students were born outside the country? All non-metric data are and always will be considered continuous. “Marketers often refer to the “levels of measurement” of a variable, a measure, or a scale to distinguish between measured variables that have different properties” (Lane, 2003). There are four basic levels which have already been pointed out, nominal,
If a researcher is going to use statistics properly, it is crucial to consider the kind of data about which descriptive statistics are to be calculated or to which will be applied some kind of statistical test. Statistical analysis, even simple analysis as counting, requires that each characteristic to be studied be assigned a unique value. Sometimes, especially in qualitative research with small samples, this value can be a word or symbol. For example, the interviewers could assign the word yes or positive to the symbol “+” to indicate that a respondent liked a product, flavor, or company. For a computer to prepare summary statistics or conduct a statistically analysis, each measurement of the sample population must be assigned a number. There are four categories which numbers are generally grouped. In increasing order of sophistication, they are (1) nominal numbers, (2) ordinal numbers, (3) intervally-scaled numbers, and (4) ratio-scaled numbers. These numbers are called data and are classified through scales.
Marketing research data is classified either metric or non-metric. Metric data are measured data, such as income, age, years or schooling. When the data are metric it can be thought to terms of average: What is the average income? Average age? Average years of school? Non-metric data on the other hand, are counted data. Gender, place of residence, and level of education are all non-metric data. When the data are non-metric we think in terms of percent-ages: What proportion of Kentwood residents are male? What percentage of the population is Hispanic? What percentage of Davenport students were born outside the country? All non-metric data are and always will be considered continuous. “Marketers often refer to the “levels of measurement” of a variable, a measure, or a scale to distinguish between measured variables that have different properties” (Lane, 2003). There are four basic levels which have already been pointed out, nominal,