One can just as easily get by without creating a scale or index, but at some point, at least in estimating the reliability and validity of your study, you're going to have to look at item and response patterns. Do the items (questions) you're asking fit together in the most productive way, or do they overlap redundantly? Do the response patterns (answers) hint at ways you can improve your measuring instrument? There's a big difference between scaling and scoring a test, and since most readers are familiar with the typical multiple choice tests found in education, that's where we'll start. it's not uncommon for social sciences to draw upon the field of Education Statistics.
A scale is a cluster of items (questions) that taps into a unitary dimension or single domain of behavior, attitudes, or feelings. They are sometimes called composites, subtests, schedules, or inventories. Aptitude, attitude, interest, performance, and personality tests are all measuring instruments based on scales. A scale is always unidimensional, which means it has construct and content validity. A scale is always at the ordinal or interval level, but it's conventional for researchers to treat them as interval or higher. Scales are predictive of outcomes (like behavior, attitudes, or feelings) because they measure underlying traits (like introversion, patience, or verbal ability).
The Gallup data helps us understand the voter perspectives as regards the integrity of the voting system and processes. However, it should be noted that the information is based upon a small sampling of the nation as a whole, and that the area where the poll would be conducted would have an impact on the data yield. For instance, voters on the east coast might have different political leanings based on their geographical concerns, careers, environmental, social and other life factors that voters feel should be impacted or addressed by the government. The poll as given in this example tells us that the information should be regarded with caution.
The aftermath of Hurricane Katrina, which left the city of New Orleans devastated in way that it has yet to recover, is the a perfect case for a study of health related issues arising out of that devastation. Public facilities like sewer and water were compromised in such a way that event that the impact of the health related conditions are as of yet unrealized. Studies should be set up to conduct quantitative and qualitative research on health related conditions such as breathing ailments, conditions related to fungus and other allergens that are residual factors of such a massive natural disaster. Perhaps the most significant study would be one related to physical and psychological impact of the disaster on the population that remained in the city vs. The population that were evacuated from the city. It would be interesting to note as to whether or not the stress related conditions would produce similar or different stress conditions in those two groups of individuals.
The elements of the classical experimental design are the independent and dependent variables, the control group, and the experimental group. In the experimental group the independent variable is manipulated. In the control the dependent variable is measured when no alteration has been made on the independent variable. The dependent variable is measured in the experimental group the same way, and at the same time, as in the control group. The prediction is that the dependent variable in the experimental group will change in a specific way and that the dependent variable in the control group will not change.
Specifically, we may be interested in knowing to what extent scores obtained for our targets are generalized over a different set of judges or items. More precisely, how much of the variance between subjects' scores is true variance? This is precisely the definition of reliability in classical experimental design. Stated differently, reliability is the squared correlation between subjects' observed scores and their (unknown) true scores. If reliability is high, scores we actually observe can be said to be highly correlated with the unknown true scores we would ideally like to measure. Thus, reliability is a crucial component of adequate measurement. For these…