1. 02.10.08 POLI 399<br />Quantitative Research: Concepts, Operationalization and Measurement<br />Or, Getting Down to the Nitty Gritty<br />Start with a concept, and then move into Variables.<br />Begin at the level of abstract theory. Your causal model should reflect this. (Deductive.) Top-down.<br />If you begin from the data (Inductive) you develop concepts from this. Bottom-up.<br />Before you measure something, you need to know what that thing is. <br />Operationalization: brings a concept down from abstract into the real and observable & measurable. Makes the concepts “kickable”.<br />Looking for accuracy and precision. <br />Concepts are abstract, but variables are concrete and measurable. Must define what you want to measure. YOUR DEFINITION MATTERS. They are not givens, it is therefore important to define it well. Definitions are contested.<br />Diagram: Operationalization Diagram<br />-Should have a third branch<br />-Top, abstract concept, moves down into more specifics <br />-Makes the abstract concept “kickable”<br />-The more you “tease things out” the more you can begin to measure<br />*NOT REQUIRED FOR PAPER, BUT A GOOD IDEA IF THE CONCEPT IS VERY MULTIDIMENSIONAL. Helps keep you clear, but also for those reading the paper.<br />When operationalizing, avoid –CAUSES –CONSEQUENCES – CORRELATES<br />CAUSE: Using the cause of the thing you want to measure, instead of what you want to measure.<br />Age or income.<br />CONSEQUENCES: Using the result of the thing you want to measure, instead of what you want to measure.<br />Vote for Conservative. Voting is a behavior! Conservatism is a belief system. <br />CORRELATE: Using a related concept of the thing you want to measure, instead of what you want to measure.<br />Religious fundamentalism. It is related, but not the same thing!<br />May measure these things to help explain, but remember that they are not the thing you are trying to explain!<br />Must justify the choices that you make. JUSTIFICATION. JUSTIFICATION. JUSTIFICATION.<br />This is important for replication. This creates RELIABLE KNOWLEDGE.<br />Must be able to explain what you did, so others can critique.<br />TRANSPARANCY. People must be able to understand what you did. In the Social Sciences, we are very skeptical of the work of others.<br />10% 35% 30% 25%<br /> -- -- -- -- <br />1 2 3 4<br />Variability, must have at least 5 cases in each cell of your cross tab. There must be variability in order to explain something. If everyone thinks the same, there is nothing to explain.<br />Precision, How precise do you need to be? Usually, it depends on what you are doing. E.g. Precisely how much you made, or categories.<br />May have to consider: what are people likely to know? How many KM did you drive? Many may not have the answer, simply because it is not the kind of info people pay attention to.<br />OBJECTIVE: Does not require people to tell you what they think. E.g. Number of arrests and convictions.<br />SUBJECTIVE: What people think exists. NOT THE SAME AS WHAT REALLY IS. <br />-No right or wrong answer in this category.<br />What people think exists, and what actually exists are two very different things. <br />Make sure categories are exhaustive. People don’t always fit with possibilities, OTHER and a ‘PLEASE SPECIFY’ can help with this.<br />Categories should be mutually exclusive. If people can only check one box, they must be able to only fit into one box.<br />NOMINAL VARIABLE: Mutually exclusive, but very little to be done with them. E.g. Gender, Religion, Vote Choice. They are categories. The number assigned to them in SPSS is irrelevant.<br />ORDINAL VARIABLE: Refers to categories for which there is an order but little else. E.g. level of education. The order of the categories means something, the order is intuitive. Distance between the categories may not always be equal.<br />Also, please circle your opinion is also an ordinal measure<br />If you have to look at the label, it is not the highest level!<br />INTERVAL VARIABLE: The categories are equal. These are hard to come by in the Social Sciences. E.g. Income, Age, Income measured in dollar figures, Number of Children <br />There is a wholeness about these figures. (An extra child is one whole extra child!) There is a natural zero in these measurements.<br />HOW DO YOU KNOW WHAT IS WHAT? SCHEME:<br />1) Is there an order to the categories? <br />Yes?No? Nominal (Must look at the labels, not the numbers.)<br /> <br />2) Are the intervals of equal size?<br />Yes?No? Ordinal<br /> <br />Interval<br />In Social Sciences, often treat Ordinal data as Interval level data. If you are treating ordinal data like interval data, can simply state this. Make assumptions about the data.<br />A measure can be regrouped to a lower level, but cannot regroup to make higher -level data. E.g. Two categories cannot become more categories! <br />When recoding, always recode into a different variable name.<br />Validity and Reliability – Very important concepts. Two different things!<br />Validity: The degree of fit is one example. How well does it fit the definition? Is it the true measure? <br />Reliability: How consistent are my readings across repeated measures? People may change their answers.<br />A bulls eye is a great way to think about it. How bang on is the result? Validity. How many scattered arrows are around the bulls eye? Want a tight cluster.<br />