Managing Dissimilarity In Scaling Properties Of Schedule_dd

MANAGING DISSIMILARITY IN SCALING PROPERTIES OF SCHEDULE D. Dutta Roy Psychology Research Unit Indian Statistical Institute 203, B.T. Road E-mail: [email protected] Analysis of survey data falls on two classes – (a) obtaining descriptive information about estimates of population characteristics and (b) obtaining information about relationship among different population characteristics. Former is applied in the census type and the later is in the investigational type of survey research. In the census type of survey, estimates of the characteristics, quantitative and qualitative, of the whole population and possibly of various previously defined subdivisions of it are required. Therefore analysis of data mainly reflects descriptive information about the population. These descriptive information form the basis of administrative action, either directly or after in corporation with information from other sources. On the other hand, investigational type of survey is more concerned with the study of relationship between different variates, with contrasts between different domains. In such surveys, estimates appertaining to the whole population are usually of relatively minor interest. It would be often erroneous to make conclusion about relationship among different statements or variates or domains based upon census survey data. Therefore, investigational survey data analysis is important. The critical analysis of the results of an investigational survey is much more difficult task than is the calculation of estimates and their errors in a survey of the census type. Dissimilarity in scaling properties of different items in the schedule make analysis more critical. Types of Survey

Census (Estimation of population characteristics and errors or descriptive information)

Investigational ( Analyzing relations among different characteristics of sample and making inference or relational information.)

In case of investigational survey, therefore attention should be paid to scaling similarity among different domains or variates of population characteristics or of human behaviour. Following steps may be considered in design of good schedule: 1. Framing hypotheses of the survey ; 2. Identify domain and sub domain of explanatory, intervening and dependent variables ; 3. Scaling the domain and sub domain and operationally define each domain so that they can be assessed objectively ; 4. Develop statements for assessing each domain or sub domain ; 5. Classify the response categories related to each statement ; 6. Scaling the response categories following prior studies or theory ; 7. Prepare the Table of cross tab analysis or graphical distribution to show the relationship among different response categories ; 8. If Tables and graphical representation are theoretically meaningful, then accept them otherwise start thinking from step 2. Framing hypotheses of the survey ; Investigational survey aims at testing assumed model and the result provides map of relationship among set of variables, therefore in framing hypotheses, one should pay more attention to the model development. Again, in model building, one can think of validity of schedule by correlating set of statements of specific domain. Identify domain and sub domain of explanatory, intervening and dependent variables. Each variable accounted for the survey possesses set of population characteristics or behaviour domain. Domain may be uni or multi dimensional. Researcher initially should think of the nature of domain based on the objective of the study, prior studies , group discussion with target people and on time limit. Scaling the domain and sub domain and operationally define each domain so that they can be assessed objectively. In operational definition of each domain, attention should be paid to the measurement scales (e.g. to what extent) rather on mere description. Develop statements for assessing each domain or sub domain. Statements should be in line with the attributes of each domain. Keep It Simple and Specific (KISS). Simple means unambiguous and specific refers to the culture of target group. Classify the response categories related to each statement Response categories should be classified based on theory. Number of categories depends upon the mental set up of target group so that target group will not be confused in making judgement.

Scaling the response categories following prior studies or theory. Considering scale properties, one may classify the response categories in terms of four scales as nominal, ordinal, interval and ratio. Nominal scales merely classify without indicating order, distance or unique origin. Ordinal scales indicate magnitude of relationships of more than or less than but indicate no distance or unique origin. Interval scales have both order and distance values but no unique origin. Ratio scales possess all three features. Selection of scales depends on the objective or hypotheses of study. Prepare the Table of cross tab analysis or graphical distribution to show the relationship among different response categories. Crosstabulations generally allow us to identify relationships between the crosstabulated variables. It provides insight about number and direction of response categories. Similarly graphical distribution of relationship ( positive, negative or zero ) provides above insight. Cross tabulation is specially useful in case of categorical and the later one is in case of non categorical variables. The above discussion is specially useful during schedule design or pre survey stage. But there are some conditions when researchers are confronted with data having dissimilar scaling properties or having multiple scale combinations, during that condition they can think of following strategies in analysis of data. Table 1 Managing dissimilarity in scaling properties of schedule Groups Nominal and Interval

Nominal and Ordinal Nominal and Ratio

Strategies Convert interval scale into nominal based on cutting point If no conversion Convert ordinal scale into nominal based on cut-point.

Convert ratio scale into nominal based on cutting point Interval and Converting ordinal scale Ordinal value into score or interval scale Interval and ratio No conversion is required

Statistics Chi-square Biserial or Point biserial correlation Chi-square Chi-square Correlation Correlation

Comment Loosing minor deviation effect of interval scale. Applicable only for two sets of variables. Loosing minor deviation effect of ordinal scale. Loosing minor deviation effect of ratio scale. Loosing scaling properties of order. No loss of statistical properties