Saturday, February 25, 2012

Rating Scales to measure data

Rating Scales to measure data

Scaling is the attempts to measure the attitude objectively. Attitude is a resultant of number of external and internal factors. Depending upon the attitude to be measured, appropriate scales are designed. Scaling is a technique used for measuring qualitative responses of respondents such as those related to their feelings, perception, likes, dislikes, interests and preferences.

Types of Scales

Frequently used Scales
  1. Nominal Scale
  2. Ordinal Scale
  3. Interval Scale
  4. Ratio Scale
Self Rating Scales
  1. Graphic Rating Scale
  2. Itemized Rating Scales
    1. Likert Scale
    2. Semantic Differential Scale
    3. Stapel’s Scale
    4. Multi Dimensional Scaling
    5. Thurston Scales
    6. Guttman Scales/Scalogram Analysis
    7. The Q Sort technique

Four types of scales are generally used for Marketing Research.

  1. Nominal Scale

    This is a very simple scale. It consists of assignment of facts/choices to various alternative categories which are usually exhaustive as well mutually exclusive. Examples of Nominal Scale are - credit card numbers, bank account numbers, employee id numbers etc. It is simple and widely used when relationship between two variables is to be studied. In a Nominal Scale numbers are no more than labels and are used specifically to identify different categories of responses. Example:

    What is your gender?
    [ ] Male
    [ ] Female

    Another example is - a survey of retail stores done on two dimensions - way of maintaining stocks and daily turnover.

    How do you stock items at present?
    [ ] By product category
    [ ] At a centralized store
    [ ] Department wise
    [ ] Single warehouse

    Daily turnover of consumer is?
    [ ] Between 100 – 200
    [ ] Between 200 – 300
    [ ] Above 300

    A two way classification can be made as follows

    Daily/Stock Turnover MethodProduct CategoryDepartment wiseCentralized StoreSingle Warehouse
    100 – 200
    200 – 300
    Above 300

    Mode is frequently used for response category.

  2. Ordinal Scale

    Ordinal scales are the simplest attitude measuring scale. It is more powerful than a nominal scale in that the numbers possess the property of rank order. The ranking of certain product attributes/benefits as deemed important by the respondents is obtained through the scale.

    Example 1: Rank the following attributes (1 - 5), on their importance in a microwave oven.

    1. Company Name
    2. Functions
    3. Price
    4. Comfort
    5. Design

    The most important attribute is ranked 1 by the respondents and the least important is ranked 5. Instead of numbers, letters or symbols too can be used to rate in a ordinal scale. Such scale makes no attempt to measure the degree of favourability of different rankings.

    Example 2 - If there are 4 different types of fertilizers and if they are ordered on the basis of quality as Grade A, Grade B, Grade C, Grade D is again an Ordinal Scale.

    Example 3 - If there are 5 different brands of Talcom Powder and if a respondent ranks them based on say, “Freshness” into Rank 1 having maximum Freshness Rank 2 the second maximum Freshness, and so on, an Ordinal Scale results.

    Median and mode are meaningful for ordinal scale.

  3. Interval Scale

    Herein the distance between the various categories unlike in Nominal, or numbers unlike in Ordinal, are equal in case of Interval Scales. The Interval Scales are also termed as Rating Scales. An Interval Scale has an arbitrary Zero point with further numbers placed at equal intervals. A very good example of Interval Scale is a Thermometer.

    Illustration 1 - How do you rate your present refrigerator for the following qualities.

    Company NameLess Known12345Well Known
    Overall SatisfactionVery Dis-Satisfied12345Very Satisfied

    Such a scale permits the researcher to say that position 5 on the scale is above position 4 and also the distance from 5 to 4 is same as distance from 4 to 3. Such a scale however does not permit conclusion that position 4 is twice as strong as position 2 because no zero position has been established. The data obtained from the Interval Scale can be used to calculate the Mean scores of each attributes over all respondents. The Standard Deviation (a measure of dispersion) can also be calculated.

  4. Ratio Scale

    Ratio Scales are not widely used in Marketing Research unless a base item is made available for comparison. In the above example of Interval scale, a score of 4 in one quality does not necessarily mean that the respondent is twice more satisfied than the respondent who marks 2 on the scale. A Ratio scale has a natural zero point and further numbers are placed at equally appearing intervals. For example scales for measuring physical quantities like - length, weight, etc.

    The ratio scales are very common in physical scenarios. Quantified responses forming a ratio scale analytically are the most versatile. Rati scale possess all he characteristics of an internal scale, and the ratios of the numbers on these scales have meaningful interpretations. Data on certain demographic or descriptive attributes, if they are obtained through open-ended questions, will have ratio-scale properties. Consider the following questions :

    Q 1) What is your annual income before taxes? ______ $
    Q 2) How far is the Theater from your home ? ______ miles

    Answers to these questions have a natural, unambiguous starting point, namely zero. Since starting point is not chosen arbitrarily, computing and interpreting ratio makes sense. For example we can say that a respondent with an annual income of $ 40,000 earns twice as much as one with an annual income of $ 20,000.

Self rating scales

  1. Graphic Rating Scale

    The respondents rate the objects by placing a mark at the appropriate position on a line that runs from one extreme of the criterion variable to another. Example

    (poor quality)
    (bad quality)
    (neither good nor bad)
    (good quality)

    BRAND 1

    This is also known as continuous rating scale. The customer can occupy any position. Here one attribute is taken ex-quality of any brand of icecream.


    BRAND 2

    This line can be vertical or horizontal and scale points may be provided. No other indication is there on the continuous scale. A range is provided. To quantify the responses to question that “indicate your overall opinion about ice-ream Brand 2 by placing a tick mark at appropriate position on the line”, we measure the physical distance between the left extreme position and the response position on the line.; the greater the distance, the more favourable is the response or attitude towards the brand.

    Its limitation is that coding and analysis will require substantial amount of time, since we first have to measure the physical distances on the scale for each respondent.

  2. Itemized Rating Scales

    These scales are different from continuous rating scales. They have a number of brief descriptions associated with each category. They are widely used in Marketing Research. They essentially take the form of the multiple category questions. The most common are - Likert, Sementic, Staple and Multiple Dimension. Others are - Thurston and Guttman.

    1. Likert Scale

      It was developed Rensis Likert. Here the respondents are asked to indicate a degree of agreement and disagreement with each of a series of statement. Each scale item has 5 response categories ranging from strongly agree and strongly disagree.

      Strongly agree
      Strongly disagree

      Each statement is assigned a numerical score ranging from 1 to 5. It can also be scaled as -2 to +2.


      For example quality of Mother Diary ice-cream is poor then Not Good is a negative statement and Strongly Agree with this means the quality is not good.

      Each degree of agreement is given a numerical score and the respondents total score is computed by summing these scores. This total score of respondent reveals the particular opinion of a person.

      Likert Scale are of ordinal type, they enable one to rank attitudes, but not to measure the difference between attitudes. They take about the same amount of efforts to create as Thurston scale and are considered more discriminating and reliable because of the larger range of responses typically given in Likert scale.

      A typical Likert scale has 20 - 30 statements. While designing a good Likert Scale, first a large pool of statements relevant to the measurement of attitude has to be generated and then from the pool statements, the statements which are vague and non-discriminating have to be eliminated.

      Thus, likert scale is a five point scale ranging from ’strongly agreement’to ’strongly disagreement’. No judging gap is involved in this method.

Opinion Scale/Likert Questions

Opinion Scale/Likert Questions

Opinion Scale/Likert Questions are usually used in surveys. The respondent is asked to state their opinion based on a scale.

Wednesday, October 26, 2011

Factor analysis

Factor analysis

Factor analysis is a form of exploratory multivariate analysis that is used to either reduce the number of variables in a model or to detect relationships among variables. All variables involved in the factor analysis need to be interval and are assumed to be normally distributed. The goal of the analysis is to try to identify factors which underlie the variables. There may be fewer factors than variables, but there may not be more factors than variables.

The main applications of factor analytic techniques are:

(1) to reduce the number of variables and

(2) to detect structure in the relationships between variables, that is to classify variables.

Therefore, factor analysis is applied as a data reduction or structure detection method (the term factor analysis was first introduced by Thurstone, 1931). The topics listed below will describe the principles of factor analysis, and how it can be applied towards these two purposes.

Multiple regression

Multiple regression

Multiple regression is very similar to simple regression, except that in multiple regression you have more than one predictor variable in the equation.

Non-parametric correlation

Non-parametric correlation

A Spearman correlation is used when one or both of the variables are not assumed to be normally distributed and interval (but are assumed to be ordinal). The values of the variables are converted in ranks and then correlated.

Simple linear regression

Simple linear regression

Simple linear regression allows us to look at the linear relationship between one normally distributed interval predictor and one normally distributed interval outcome variable.



A correlation is useful when you want to see the relationship between two (or more) normally distributed interval variables.