the poll results only those who say they are likely to vote.
Sometimes researchers face situations in which they know that units differing on some characteristic have different responses to the same question. Most experts in business activity know that large and small firms react very differently to government regulations that involve a lot of reporting; large firms can absorb it more easily because they have more people who can deal with paperwork while others make the product, sell the goods, or provide the service than do small companies. Comparative politics experts agree that urban and rural individuals have different attitudes on many questions. Gender, race, and income are known to strongly affect political attitudes on some issues. When survey researchers believe that the units they want to investigate will have different views base don some characteristic, they will make sure that their sample closely reflects how the population divides on that characteristic by using a proportionate stratified sample. In this procedure, the researcher divides the population into two or more groupings (strata) on the characteristic, and then draws random samples from the units in each grouping. Thus, if they were investigating attitudes on war, in which individuals’ attitudes are known to differ significantly by gender, researchers would identify use separate lists of males and females to identify their respondents. In a proportionate stratified sample, the size of the sample drawn from each strata is adjusted so that the proportion of respondents in each grouping matches the proportion of the grouping in the population. For a study of attitudes using genders as the strata, a proportionate stratified sample would include an equal number of males and females anywhere the gender ratio is close to 50-50. In an area where 40% of the population is male, 40% of the respondents would be drawn from the list of males, and 60% from the list of females. When a subgroup’s share of the population of units is very low, researchers may use disproportionate stratified sample to make sure that they have enough respondents from the relatively small group to get an accurate assessment of the range of views in the smaller group. A researcher surveying with genders as strata might use a sample of 300 males and 300 females; a researcher stratifying a population by ethnicity in which 48% were one ethnicity, 48% a second ethnicity, and the remaining 4% a third, might use a sample with 300 from each group. This does not mess up results if the goal is to compare attitudes within each group to attitudes within each other group. It only becomes a problem if someone forgets how the samples were drawn and assumes that the set of responses were collected from a random sample and can be used to draw conclusions about the attitudes of the population as a whole rather than about the separate groupings.
Formulating the Questions
Having a good random or proportionate stratified sample of respondents is important to a good research project. However the data is generated by asking the respondents for their answers to one or more questions. The data cannot be any better than the questions.
Pollsters seeking to predict election results have a very standard set of questions. Early in the nomination process (during primaries for example) they often try to assess the relative popularity of various candidates by asking how a person would vote if different pairs of candidates were running against each other. You will see this in early 2008 as people start thinking about which Republican hopeful can or cannot defeat which Democratic hopeful. At this point in the campaign season they can also likely voters in each party who they plan to support in the primary. You saw lots of polls of this type in late August and early September as O’Reilly, Gabrielli, and Patrick were all contending for the Democratic nomination. As the general election comes closer, the questions which to “which candidate will you vote for?” or, for the ballot questions, “will you vote yes or no on Questio