Lecture 4.docx

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Katherine( Katy) De Celles

Lecture 4: Compensate measure of variables; 1. Scale  Process of measuring or ordering entities in respect to quantitative attributes  Give those attributes a list, and group them to stronger or weaker variables  Cluster of questions, which tap into a unitary dimension of behaviour, attitude or feeling  Exp. Attribute to masculinity; Cars, sports, etc…  Measuring one concept in a scale, everything is related 2. Index  Constructed by accumulating scores assign to individual attributes  exp. Index contribute to scores in a dimension, not as specific as a scale  Combination of several empirical indicators into a single measure  Inadequacy of a single indicator  when we have multiple indicators each indicator reflects a distinct aspect of the dimensions  Compensate measurement has a better range of variation  Increases the reliability of our experiment  Similarities  Both are ordinal measures of variables  They rank order attributes  Both are compensate, measure one variable  Difference  Index: accumulate scores assigned to individual attributes  weighted on the same  Scale: Assign scores to patterns of responses  has an intensity structure among the attributes  Indexes are easier to create, scale is better  Scales are predictive of outcomes, an ordinal list  Index, everything is weighted the same, they measure all the dimensions of a topic rather than focus on one aspect.  Exp. Multiple but distinctive attribute of behaviour (exp)  Index should have high face validity 5 things to keep in mind; Index 1. Select your items carefully  Face validity (logical), related to your construct  They are unidimensional; tapping into multiple dimension but based on and measuring only one concept  Depending on your topic, it will determine the broadness of your index  Conceptualize your term 2. Examine whether there is an empirical relation  Empirical relation is established when answer to one question will predict the next question  If two items are empirically related, we can argue that each reflects the same variable, so both can be included in the same index 3. Assign scores for responses  Two basic decisions  Decide the desirable range of the index scores  Decide whether to give each item in the index equal weight or different weights  Ranking for each items 4. Handle missing data  How representative will your research be?  Exclude cases with missing data from the construction of the index and the analysis  Treat missing data as one of the available responses  Assumptions, using their non – response as a “no”  Analysis missing data to interpret the meaning  All of these methods  enters bias into one’s paper 5. Validate the index  Item analysis: internal validation  to what extent does the index as a whole to the items that formulate it?  does each item make an independent contribution to the overall measurement of one’s concept  having internal validity does not mean the items are not empirically related  External validation: ranking of groups on the index should predict the ranking of groups in answering similar or related questions  Sampling  Question: who do I want to question?  Not being confine to only the individuals they study, generalize to the population  The population is who we sample from  Studying a population as a whole is impossible, need small groups to function the research Sampling process: stages 1. Defining the population of concern; who you want to caterer to for your research 2. Specifying a sampling frame 3. Specifying a sampling method for selecting items or events
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