The New 
Precision Journalism

by Philip Meyer

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Chapter1 - Journalism and the scientific tradition
Chapter 2 - Some elements of data analysis

Chapter 3 - Harnessing the power of statistics

Chapter 4 - Computers

Chapter 5 - Surveys

Chapter 6 - More about data analysis

Chapter 7 - Field experiments

Chapter 8 - Databases

Chapter 9 - How to do an election survey

Chapter 10 - The politics of precision journalism


Sometimes your data analysis skills will be applied to data that have been collected by others, government agencies, for example. At other times you will have to collect it yourself. The most widely used method is survey research, more popularly known as public opinion polling (although many applications involve special populations rather than the general public). A survey has the following elements:

1. An information goal or set of goals.
2. A sample.
3. A questionnaire.

4. A collection method (personal interview, telephone interview, self-administered questionnaire). 

5. Coding and analysis.

Getting to an information goal was discussed in chapter 1. This chapter is about the mechanics of getting to that goal by the survey method.



General principles

The kind of sample you draw depends, of course, on the method of data collection. If you are going to do it by mail, you need a sample that includes addresses. If by phone, you need phone numbers. If in person and at home, you can get by without either of these, at least in the opening stages. You will probably use instead the census count of housing units.

Regardless of the method, the basic statistical rule of sampling still applies: 

Each member of the population to which you wish to generalize must have a known chance of being included in the sample.
The simplest way to achieve this goal is to give each member of the population an equal chance of inclusion. It needs to get more complicated than that only if you wish to oversample some minority segment. The purpose of oversampling is to make certain that you will have enough to allow you to generalize to that minority. For a study on race relations, for example, you might want equal numbers of minorities and nonminorities, even though the minorities are only 15 percent of the population. You can do that and still generalize to the population as a whole if you weight your oversample down to its proportionate size in the analysis. That is simpler than it sounds. Three lines of SAS or SPSS code are all it takes to do that trick. Here's an SPSS example:
IF (RACE NE 1) WTVAR = .3.
The first line creates a weighting variable for every case and initializes it at 1. The second causes the computer to check each case to see if it is a minority. If it is, its WTVAR is changed to .3. The third line weights the data.
For now, however, we'll consider only equal probability samples. It is easy to think of ways to do it in theory. If you want a representative sample of adults in your home town, just write all their names on little pieces of paper, put the slips of paper in a steel drum, stir them up, and draw out the needed number. If you live in a small enough town, that might actually work. But most populations are too big and complex. So samples are usually drawn in stages on the basis of existing records.
Telephone samples

One of the big advantages of telephone surveys is that the existing records make it quite convenient. Let's start with the simplest kind of telephone sample, one drawn directly from the phone book.

1. Cut the back off a telephone book so that it becomes a stack of loose pages.
2. Prepare a piece of cardboard (the kind the laundry wraps shirts around will do nicely) by cutting it to the size of the page and making four or five holes sized and shaped so that each exposes one name and number.
3. Decide how many calls you need to attempt to get the desired number. Divide the total by the number of holes in the cardboard. Call that number n, the number of pages you will need.

4. Divide the number of pages in the phone book by n. The result is i, the interval or number of pages you have to skip between sample pages.

5. Start at a random page between 1 and i. Slap the cardboard over it and hit the exposed numbers with a highlighter pen. Repeat the procedure with every ith page.

What if you land on a business number? Many cities have business and residential numbers segregated in their phone books. If yours doesn't, you will have to increase your draw so that you can throw away the business numbers and still have enough. The total number you draw will depend a good deal on the characteristics of your town, and so some experience will help. But a draw of twice the number you hope to complete is a reasonable start. Some of the people in the book will have died or moved away, some will not be at home when you call, and some will refuse to be interviewed.
As easy as this sounds, it still includes only one stage of the sample. Drawing a phone number gets you to a household, but more than one member of your target population may share that number. You need a way to randomly choose a person within the household. The equal-probability rule is still your best guide. Several methods have been devised that require you to ask the person who answers the phone to list all the eligible respondents, e.g., persons 18 and older, at that number. Then, using some random device, you choose one and ask to speak to that person. A simpler way is to ask how many persons who meet the respondent criteria specification are present and then ask in what month their birthdays fall. With that list, you can choose the person with the next birthday. Because birthdays occur pretty much at random (and because astrological sign does not correlate with anything), each person in the household has an equal probability of selection. 
Right away you can think of two things that might go wrong: 
1. Nobody is at home when you call.
2. The husband answers the phone, but the next-birthday person is the wife, and she works nights or is otherwise unavailable.
The simple solution is to call another number in the first instance and interview the husband in the second instance. But stop and think! What happens to your equal-probability criterion if you do that? It is violated, because you will have introduced a bias in favor of people who are easy to reach. To maintain the equal-probability standard, you have to follow this rule: 
Once a person is in the sample, you must pursue that person with relentless dedication to get his or her response. Any substitution violates the randomness of the sample.
For no-answers, that means calling back at different times of the day and week. For not-at-homes, that means making an appointment to catch the respondent when he or she is at home.
Of course, there has to be some limit on your hot pursuit. And you need to treat all of your hard-to-get potential respondents equally. To chase some to the ends of the earth while making only desultory attempts at others would violate the randomness principle. So you need a formal procedure for calling back and a fixed number of attempts. Set a level of effort that you can apply to all of your problem cases. 
Your success will be measured by your response rate. The response rate is the number of people who responded divided by the number on whom attempts were made. If you dial a telephone and nobody ever answers, that represents one person on whom an attempt was made -- even though you may know nothing about the person.

What is a good response rate? Years ago, when the world was a gentler and more trusting place, response rates of more than 80 percent were commonplace in personal interview surveys, and that became more or less the standard. By the late 1980s, researchers felt lucky to get two out of three. As the response rate falls below 50 percent, the danger increases rapidly: the people you miss might differ in some systematic and important way from the ones who were easier to reach.

An example will illustrate why this is so. Suppose your information goal is to learn how many members of the National Press Club are smokers. Your mail survey has a response rate of 80 percent. Now assume a major bias: smoking has become a mark of low sophistication and ignorance. Smokers, loath to place themselves in such a category by admitting their habit, are less likely to respond to your questionnaire. Their response rate is 10 percent, compared to 50 percent for nonsmokers. The following table is based on a fictional sample of 100.


As you can see, the true value in the population is a smoking rate of 20 percent. But among those who responded, it is only about 5 percent (2/42). That's an important underestimate. If you go back to the nonrespondents for a second wave of data collection, you are more likely to pull in smokers, simply because there are proportionately more of them to be found. The fewer nonrespondents, the less room is left in which the bias can hide.

Because every research project is subject to the first law of economics - -i.e. nobody has enough of anything to do everything -- you have to consider a tradeoff in your design between sample size and sample completeness. Follow this general rule: 

A small sample with a good completion rate is better than a large sample with a bad completion rate.
One reason for this rule is a healthy fear of the unknown. You know the effect of shrinking the sample on your error margin. But the error introduced by systematic nonresponse is unknowable.


A better telephone sample

The method just described has a couple of flaws. If you choose each listed household with equal probability of selection in the first stage and select a member from the chosen household with equal probability in the second stage, that doesn't add up to equal probability. Why not? Because households come in different sizes. Assume that the first household in your sample has one adult of voting age and the second has three. Once the second sampling stage is reached, the selection of the person in the first household is automatic, while the people in the other household must still submit to the next-birthday test. Therefore, the single-person household respondent has three times the probability of being selected as any of the three persons in the second household. The best solution is to use weights. The person you choose in the three-person household is representing three people, so count him or her three times. (That's relatively speaking. More specific advice on weighting will come in the analysis chapter.)

Here's another complication in telephone sampling: in this age of telecommunications, some households have more than one telephone line. The extra one may be for the children, a computer, a fax machine, or a home office. If both phones are listed, the two-phone household has twice the probability of inclusion. You can correct for that by further weighting, but first you have to know about it, and you can do that by asking. Just make one of your interview questions, "Is your household reachable by more than one telephone number, or is this the only number?" If there is more than one, find out how many and weight accordingly.

If you do all of the above, you will have a pretty good sample of people whose households are listed in the phone book. Is that a good sample? Yes, if all you want to generalize to is people listed in the phone book. Most of the time you will have a more ambitious goal in mind, and a phone book sample can mean trouble. On average, across the United States, 15 percent of the working residential numbers will be missing from the phone book. That proportion varies widely from place to place, so check it out in your locality. Most of the nonpublished numbers belong to people who moved in since the phone book was published. Others are unlisted because the householder wants it that way. Maybe he or she is dodging bill collectors and former spouses or is just unsociable. Either way, such people are out of your sampling frame.

There is a way to get them back in. It is called random digit dialing, or RDD. You can draw your own RDD sample from the phone book, using the listed numbers as the seed. Follow the procedure with the holes in cardboard as before. But this time, instead of dialing the published number, add some constant value to the last digit, say 1. If you draw 933-0605 in the phone book, the sample number becomes 933-0606. And it could be unlisted! That method, called "spinning the last digit," will produce a sample that comes very close to fulfilling the rule that each household have an equal chance of being dialed.

Of course, some of those numbers will be business numbers. And some will be nonworking. If a human voice or a recording tells you that the number belongs to a business or is nonworking, you can pitch it out of the sample. Unfortunately, not all nonworking numbers are connected to a recording machine. Some just ring into empty space, like the philosopher's tree falling in the forest where no human ear can hear. That means you really can't figure an absolute response rate (successes divided by attempts on real people), because you don't know if there is a real person associated with the number the interviewer hears ringing. Best bet in that case: specify some reasonable number of attempts on different days and at different times. Then if there is no answer, chuck it out of the base. But remember you will have to redefine your sample base, not as all possible numbers, but as all numbers verified to be working. That is a big difference, but it is still a rate worth calculating, because you can use it to compare your completeness from one survey to another.

Using the telephone directory as an RDD seed is convenient, but it may not be a completely random seed. In a larger city, the three-digit prefixes are often distributed in some geographic pattern that might correlate with the socioeconomic characteristics of the subscribers. As a result, certain prefixes (or NNX's, as the phone company calls them) will have more unlisted numbers than others. An area with an unusually high proportion of unlisted numbers is underrepresented in the book and will still be underrepresented in any RDD sample drawn from that seed.

The best solution to this problem is to avoid the phone book altogether. Obtain from your local telephone company a list of the three-digit prefixes and an estimate of the number of residential telephones associated with each plus a listing of the working ranges. Phone companies tend not to assign numbers at random but to keep them together in limited ranges. You can save time and effort if you know those ranges and don't have to waste time dialing in the vast empty spaces. From those data, you can estimate how many calls you need to complete from each NNX and you can write a short program in BASIC or SAS to generate the last four digits of each number randomly but within the working ranges. Sound like a lot of trouble? Not really. Here is a BASIC program for printing 99 four-digit random numbers:

10 FOR I = 1 TO 99
20 PRINT INT(RND*8000)
This method works for large areas, including states, provided the number of telephone companies is limited. Maryland is relatively easy because most of the state is covered by one company. North Carolina is tough, having more than thirty companies to contend with.
Telephone sampling has become such a specialized task that many survey organizations prefer not to do it themselves and instead contract the job out to a sampling specialist who charges by the number. A statewide sample for one-time use for a few hundred dollars was a typical price in 1990.


Household sampling

The discussion of telephone sampling assumed that the universe of telephone households and the universe of all households are one and the same. If you have the good luck to be doing survey research in Sweden, that's just about true. Telephone penetration there is 99 percent. Canada is good, too, with 97 percent. In the United States, however, only 94 percent of households have telephones. In some states in the South, coverage is much lower.1

For some news stories, a telephone sample won't do. You may need the nontelephone households because you want the downscale segment represented. Or you may have to visit the respondent in person if you want the interviewer to show an exhibit or size up the person's appearance or walk in the house and inspect the contents of the refrigerator. The objective of equal probability for all can be met for personal interviews, but with some difficulty.

If you are going to do 1,500 interviews in your state or town, you will want to cluster them to reduce field costs. Like telephone samples, personal interview samples are based on housing units. You can even use the phone book. Draw a sample of telephone listings in the manner already described, but with this difference: divide the number selected by five. That gives you a sample that, after allowing for not-at-homes and refusals, would yield 300. But those are 300 clusters, not 300 interviews.

Send an interviewer to each address with the following instructions:

1. Do not attempt an interview from the listed address.
2. Stand with your back to the listed address, turn right and take the household next door. (If in an apartment building and there is no unit to the right, go downstairs one flight and start with the last one on the left, then work to the right.)
3. Continue in this manner. If you come to a corner, turn right, working your way around the block, until you have attempted five housing units.
An even better way is to send a crew out into the field to prelist the units in all of the clusters. In that way, the interviewer doesn't have to waste time figuring out the instructions, and you have time to resolve any ambiguities.
Because the household that forms the seed for this sample is skipped, those not listed in the directory have an opportunity to be included. There is still a bias, however, against neighborhoods with high proportions of unlisted numbers or no telephones at all.
Using the census

When your population is too scattered to be covered by one or any other convenient number of phone books, or when you are concerned by the no-telephone/unpublished-number bias, consider skipping phone books and working directly from census counts.

Assume that you want a statewide survey. Draw the sample in stages. Start with a listing of counties and their populations. If your survey is about voting, use the number of registered voters or the turnout in the last comparable election instead of total population.

Your goal is to choose sample counties with representation proportional to population. Divide the population by the number of clusters needed. If you plan to attempt 1,500 interviews (and hope for 1,000 at a 67 percent response rate), you will need 300 clusters of five. Take North Carolina, for example. Its 1988 census population estimate was 5,880,415, and it has 100 counties. Dividing the total population by 300 yields 19,601. That will be the skip interval. Now take a walk with your pencil down the list of counties and find out in which counties each 19,601st person falls. Start with a random number between l and 19,601. Where to get such a random number? Books like this one used to publish long lists of computer-generated random numbers just to help out in such cases. With personal computers and calculators so handy, that is no longer necessary. Once you have learned BASIC, you can use its random-number generating capability. Meanwhile, just grab your calculator and multiply two big, hairy numbers together. Skip the first digit, and read the next five. If they form a number equal to 19601 or smaller, use it. If not, move one digit to the right and try again. If necessary, enter another big hairy number, multiply and try again. Let's assume you get 3,207 (which is what I just drew by following my own instructions). Call this number the random start.

To show you how this works, I am going to walk you through a lot of numbers very quickly. But don't even think of looking at the next few paragraphs until you have the concept. Here is another way to get it. Imagine all of North Carolina's people lined up in a long queue, by county, in alphabetical order. The object is to find the 3,207th person in the line, and then every 19,601st person after that. If we count them off that way we will collect 300 people, and we will know what counties they came from. Each of those persons represents one sampling point in his or her county. The object of this exercise is simple: to find out how many sampling points, if any, each county gets. By basing the selection on people, we will automatically give each county representation according to the size of its population. Some small counties, with populations less than the 19,601 skip interval, will be left out. But some will fall into the sample by chance, and they will represent all of the small counties.

If you understand the concept, its okay to go ahead and look at the example. Or you can wait until such time as you need to actually draw a sample. The example is just to show the mechanics of it.

Here is the top of the list of North Carolina's 100 counties.



Your first task is to find the county with the random start person, in this case the 3,207th person. That's easy. It is Alamance. Subtract 3,207 from the Alamance population, and you still have 95,929 people left in the county. Your next person is the one in the position obtained by adding 3,207 and 19,601. But don't bother to do that addition. Just subtract 19,601 from the 95,929 still showing on your pocket calculator. The result shows how many Alamance County people are left after the second sample hit. There are still 76,328 to go. Keep doing that and you will find that Alamance gets five sampling points and has 17,525 people left over.

Subtract 19,601 from that remnant, and you get negative 2,076 which means that your next selected person is the 2,076th one in the next county, Alexander. Keeping track of this process is simple. To get rid of your negative number, just add in the population of Alexander county. Now subtract 19,601 and you have 3,322 left. Because this remainder is less than 19,601, Alexander gets no more sampling points.

To get rid of the negative, add in the population of the next county. Little Alleghany County at 9,587 doesn't quite do it; there is still a negative remnant. No sampling point at all for Alleghany County. Add in Anson County. It has enough population for one hit, but with 19,140 left over it doesn't quite qualify for a second. Subtracting the skip interval yields the negative that shows how far into the next county our target person waits. And so on and on. If you follow this procedure all the way through North Carolina, you would end up with exactly 300 sampling points.

For each of those chosen counties, you next need to get the detailed census maps that show tracts. In this stage of the selection you give each tract an equal probability of selection, regardless of its size. That makes it easy. If a county needs five sampling points, add up the number of tracts and divide by five to get the skip interval (i). Choose a random start. Take every ith tract or district.

In the final stage, choose blocks with probability proportional to population. It is the same procedure used to choose the counties, only on a smaller scale. The blocks become your sampling points.

Now you need to devise a rote procedure for choosing a starting point in each block. You can't let the interviewer choose it, because he or she will pick the nicest looking or the most interesting looking place. Tell her or him to find the northeast corner of the block and then choose the second dwelling to the right. Starting with the corner house is considered a bad idea because corner houses might be systematically different -- more valuable in some older neighborhoods, less valuable in others because of the greater exposure to traffic. In neighborhoods without clearly defined blocks, you will have to use some other unit such as block group. Maybe you will have to throw a dart at a map to get a starting point. Just remember the first law of sampling: every unit gets an equal chance to be included.

When the starting point is chosen, give the interviewer a direction, and then take five dwellings. If you can prelist them in the field first, so much the better.

In multistage sampling it is important to alternate between selection proportional to population and equal probability of selection. That adds up to equal probability for the individuals finally chosen. Leslie Kish gives the arithmetic of it in his authoritative work on the subject.2  I can explain it better with an example.

Consider two blocks of high-rise apartments. Block A has 1,000 households. Block B has 100.

If you live in block A you have 10 times the probability of having your block chosen.

But here is the equalizer: the same number of interviews is taken from each block. So once the blocks are chosen, a person living in Block B has 10 times the probability of being interviewed as a person in a selected Block A. The bottom line: equal probability for all.

When you cluster a sample to save time and trouble in the field, the arithmetic of sampling changes. Kish gives the mathematics for figuring it exactly. For a rough rule of thumb, figure that clustering cuts efficiency by about a third. In other words, a cluster sample of 1,000 would yield about the same margin of error as a pure probability sample of 666.

Some of the efficiency that is lost in clustering is regained by stratifying. The procedure described for North Carolina ensures that sampling points will be geographically scattered and that no major county will be left out and that the biggest counties will have respondents in proportion to their size. Because none of those things is left to chance, you get some improvement over simple randomness.

Samples of limited areas

For the 1967 Detroit riot survey, John Robinson designed a sample that used census and city directory data without clustering. Because the geographic area was so small, there was no great advantage to clustering households. But we did cluster within households. Teenagers as well as adults were included in the sample, so Robinson specified that half the eligible respondents would be interviewed in each home. They were chosen by making a numbered list, based on sex and age, and then taking all of the odd (or even) numbers. Making participation a family activity helped boost cooperation, although it created some difficulty in protecting privacy. A city directory was used to obtain the addresses, and Robinson devised a procedure for getting unpublished addresses. Each interviewer checked the house next door to the house in the sample. If that house was not listed in the directory, interviews were taken there as well. To the extent that unlisted houses next door to randomly chosen houses are a random sample of all unlisted houses, that brought them in with correct representation.

Bias in telephone and home samples

The people most difficult to reach tend to be those at the bottom of the socioeconomic scale. Interviewers don't like to go into bad neighborhoods, and telephone penetration is also less in those kinds of neighborhoods. Telephone surveys introduce an additional bias against less-educated people, who are less likely to cooperate with a telephone interviewer once they are reached on the telephone. In some kinds of surveys, this does not make a lot of difference. If it is a marketing survey, the nonrespondents tend to be nonbuyers as well. If it is a voting survey, they are likely to be nonvoters. But the upper-class bias can be a serious defect for many surveys for journalistic purposes. If the topic involves a social problem, the people most affected by the problem may be the ones least likely to be reached by a survey.

In Miami, when Juanita Greene, George Kennedy, and I studied the black community before any rioting had taken place there, we were surprised to find our data telling us that two-thirds of all the blacks in Miami were female. This was the first time we had encountered the problem of the invisible black male. How can one handle such a profound bias in the sample? We considered several choices: 

1. Weighting. We could weight up the males we did get to make them represent the males we didn't get. Problem: chances are pretty good that the ones we didn't get are different, maybe a lot different, from the ones who could be found.
2. Throw the data away. Problem: we didn't know how to collect data that would be any better. 
3. Redefine our sampling frame and generalize only to the stable, visible black population. Problem: redefining the missing males out of the survey doesn't really make them go away.
We chose the third option, and Greene used conventional reporting methods to write a separate story on Miami's invisible black males and the social and political forces that kept them out of sight. She showed with anecdotes what we could not show with data: that the family structure and the welfare regulations forced poor males into a state of homelessness and/or disaffiliation with families. That strategy covered that base and left us free to write about the data from the survey with frank acknowledgment of its limitations. And it suggests a pretty good general rule: 
When writing about a social problem that involves people who are going to be underrepresented in your survey, find some other reporting method to include them in the story.
Knowing when a survey can't carry all of the freight will keep you from deceiving yourself and your readers.
Sampling in mail surveys
Mail surveys are usually done for special populations. Getting the mailing list can take some reportorial ingenuity. When Mike Maidenburg and I did a five-year follow-up survey of people who had been arrested in the first major student protest of the 1960s ñat Sproul Hall on the campus of the University of California in 1964 ñ we worked from alumni records. But first we had to know who had been arrested, and the courts had expunged the records of every person who was under the age of 21 at the time of the arrest. Fortunately, the order to expunge had taken some time, and local newspapers had printed their names while they were still available. A search of those contemporary newspaper accounts produced the needed list of names, which could then be compared with the alumni list for current addresses.

USA Today needed a list of inventors for a story on the current state of American ingenuity. It obtained a list for a mail survey by checking the U.S. Patent Office for recent registrations. Mail surveys are commonly used to profile delegates to the major party nominating conventions, and the names and addresses are available from party headquarters. Surveys of occupational groups, such as policemen and airline pilots, have been done by using lists obtained from their professional associations.

Sometimes the target group will be small enough that no sampling is needed. You can attempt to collect data from each member of the group. But the basic rule of sampling still applies: completion is more important than sample size. If your target population has 8,000 names and addresses, you can send a questionnaire and get perhaps 2,000 back. That 2,000 is a sample, and not a very representative one. But if you sampled every fourth name to begin with, sent 2,000 questionnaires and did vigorous follow-up to complete 1,500 of them, you would have a far superior sample.

When you sample from a small population, the margin for sampling error is reduced somewhat, though not as much as you might think. George Gallup liked to explain it with an image of two barrels of marbles. One barrel holds 200,000 marbles, the other 2,000. In both barrels half the marbles are black and half are white, and they are thoroughly mixed. Scoop out a handful from either barrel and your chances of getting close to a 50-50 mix are about the same. Each individual marble has an even chance of being black, regardless of the size of the barrel from which it came.

But when the population is very small, the chances of sampling error are appreciably reduced. The rule of thumb: if your sample is more than one-fifth of the population being sampled, try the correction factor.3  The formula:

sqrt (1-n/m)

where n is the sample size and m is the population from which it is drawn. Work it out, and you'll see that if your sample of 2,000 is drawn from a population of 8,000, the error margin is 87 percent of what it would be if the population were of infinite size.

Possibility samples

There are situations where scientific sampling is not possible, but there is still some point in using some kind of random selection. Intercept or shopping mall interviews are an example. There is a crowd of people and no way to get them to hold still for sample selection. But you can pick a random point in the mall and a random number, say 4. Stand at the random point and count the people who cross an imaginary line and then intercept the fourth one. That at least prevents the choice from being made by the interviewer, who is likely to prefer people who look interesting, attractive, sexy, or otherwise appealing. Since the probability of any given shopper crossing your random point is unknown, it is not a true probability sample, but it at least eliminates interviewer selection as a source of bias. This technique was used by Al Gollin to sample participants in mass political demonstrations when he was with the Bureau of Social Science Research.4 Stanley Milgram used it for assessing the helpfulness of people encountered on city streets.


The answers you get depend on the questions you ask. In recent years, because of an increasing awareness of the sensitivity of certain kinds of issues to the design of the survey instrument, it is necessary to question not only the wording of survey questions but also the order and manner in which the questions are asked. Survey questions are carefully framed in everyday language to be understood by everyday people, and yet the survey situation is quite different from everyday conversation. Listen to somebody else's spontaneous conversation on an elevator or in a taxi and notice its structure. It is full of redundancies. Its questions and answers cover the same material over and over as the participants reduce ambiguity and converge on a narrow area where both desire some level of precision in understanding. Consider the redundancy checks in your own telephone conversations. Notice how a phone conversation usually ends with each party repeating what he or she is going to do next as a result of the conversation, even though that ground has already been well covered. Instinctively, we know how difficult oral communication is, and we build in the redundancies as a form of error checking.

Questions in survey research are put in a much different framework. The purpose is to measure a response to a stimulus, and so the stimulus must be formulated in a way that can be repeated from one respondent to another so that each respondent is reacting to exactly the same thing. The questioner cannot improvise or recast the question to fit the existing knowledge or interest of the respondent. Each item has to be delivered just the way it left the question factory.

That procedure pays off in precision, but it comes at the cost of creating an unnatural situation in which the full power of oral communication is not realized. The survey question-asking situation is so unnatural that Howard Schuman, in his 1986 presidential address to the American Association for Public Opinion Research, argued for de-emphasizing, or even ignoring altogether, the raw frequencies or marginals in survey results. No survey number means very much, he said, without another number to compare it to. Knowing that 60 percent of the conference attendees liked the conference program would be good news, he said, if the average for previous conferences had been 40 percent. But "if the average over the past years had been 80 percent, this year's organizers might well hang their heads in shame."5

The referendum model

Schuman's view is contrary to most journalistic practice, which is to treat polls as an ongoing referendum in which the people instruct their representatives on how to act. That model can lead editors, politicians, and readers alike to overestimate both the power of the survey question and the knowledge and attentiveness of the typical citizen.

And yet the referendum model is not always invalid. If it were, polls would not predict elections as accurately as they do. And questions on many public policy issues would not show the robustness that they do. By robustness I mean that some questions keep giving the same answers, no matter how you twist or tamper with them.

That leads to the first law of question writing:

Never pass up a chance to borrow or steal a question that has worked for somebody else.
The advantages are several. If it worked for somebody else, it is more likely to work for you. And you already have another population and/or another time with which to compare your population at your time.
Here is another general rule:
Do not frame a question to fit a headline that you hope to write. 
Some of my best friends are newspaper editors, but I hate writing questions with them when they are looking ahead to the kind of headline that they hope to get. An editor's question about the public's response to the president's latest tax proposal might read something like this:
Which of the following best describes your response to the president's tax proposal: 
1. I like it.
2. I sort of like it.
3. Drop dead!
The hope is that the headline writer can later say something like "People to Prez: Drop Dead!"
Even if you tried such questions, you would find that most of the time respondents will shy away from the catchy headline-grabbing response in favor of a more conventional one. And you have hopelessly biased the question by changing its tone in mid-thought, leaving it badly out of balance. It just does not pay to try to put catchy phrases in your respondents' mouths.


Open-ended questions

The other extreme, putting no phrases in the respondent's mouth by asking a question that is open-ended, is equally impractical for most journalistic purposes. When an open-ended question is asked, the answers have to be recorded, coded, and categorized in some way if they are to be summarized. Just developing the coding scheme can be a long and tedious process. You have to look at the answers produced and figure out ways to classify them. Once a classification scheme is worked out, you then must go over each response and decide where it fits in the scheme. In a business with daily deadlines, there are just two situations where open-ended questions are useful: 

1. When you use them to generate quotes to liven up a story. You don't need to code or classify them in that case.
2. When the response is a number, e.g., "How many years have you lived at this address?" Quantitative information can be entered directly into the computer, as long as the unit is consistent.
In most other situations, open-ended questions are a poor option for journalistic surveys. When under deadline pressure, you have to constrain the responses to categories that can be counted and compared in the computer with a minimum of human processing. And so the closed response categories become an important part of the question, both leading respondents to the categories that you picked in advance and shunting them away from all the possibilities you are not giving them. It is a big responsibility.



The chief disadvantage of the closed-end response is that the respondent with no knowledge of the subject can pick one of the proffered choices as readily as one who is well versed. Indeed, the social pressures of the interview situation encourage it. The interviewer defines the roles: I give questions, you give answers. And the system forces everyone into a category. Many journalists are disappointed when large numbers of respondents' answers fall into the "don't know" category, and argue for question protocols that force a respondent to decide. But all that such a practice does is contribute to self-delusion. Lots of people really don't know, and as a journalist/researcher you should feel that it is as important to know and count them as it is to identify the people with firm intentions. Thus the rule:

"Don't know" is data.

Cherish it as much as the data from people who do know. More than twenty-five years ago, Philip Converse started worrying about the measurement of what he later called "non-attitudes" when he was a survey respondent and noticed himself hastening to give answers to questions on topics about which he knew or cared little, just so he could fulfill his role in the social encounter and get it over with. That led to a career-long interest in the subject and some seminal work that has led to a greater appreciation for "don't know" as valuable data.6 Later, two other University of Michigan researchers, Howard Schuman and Stanley Presser, experimented with questions that contained explicit invitations to admit to not knowing. They found that the relative proportion of pro and con positions often remained unchanged, but the number of don't knows increased substantially.7 How do you invite the respondent to admit not knowing? Here is an example:

"Do you think the United Nations has been doing a good job or a poor job in dealing with the problems it has had to face ñ or haven't you followed this closely enough to have an opinion?"

To demonstrate the importance of including this escape hatch, Schuman and Presser, along with George Bishop of the University of Cincinnati, asked people to give their opinions on some things that don't exist, such as "The Public Affairs Act." Almost a third expressed an opinion. When the escape hatch was added to the question, fewer than 10 percent pretended knowledge of the nonexistent act.8

Another way to avoid the non-attitude problem is to put a don't know filter ahead of the question. Ask a simple knowledge question first, e.g., "Have you read or heard anything about . . ." If the answer is no, don't bother the respondent with a question on that topic.

Interest groups that use polls to generate political support for their causes often have extremely esoteric concerns that they try to fit to the referendum model. They do it by drafting a very long question that explains the issue and then asks the respondent to take a side. Not a good idea! You just can't create instant education that way and then generalize to what the rest of the public would think if it were well informed. The question becomes so complicated that it is almost impossible to word objectively. And the instantly-educated respondent thus created is not representative of anybody. The instant education makes the respondent different from other ignorant respondents without bringing him or her up to speed with those who have studied and thought about the issue. It is far better to identify those who are already well informed, and then ask them what they think. 

Journalists are particularly likely to fall into the trap of thinking that their concerns and interests and knowledge are reasonably representative of the population as a whole. They're not! If you are reading this book, that alone marks you as a peculiar, even deviant, subset of the population of journalists and journalism students, not to mention the population as a whole. Never generalize from yourself. For that matter, never generalize from Chapel Hill or Manhattan, Kansas, or Milton, Florida, or any interesting place where you happen to live. Representativeness is elusive, and it is somewhere else.

The middle-category problem

When the Harris Survey asks for a rating of the president's performance, the choices given are "excellent, good, fair, or poor." When the Gallup Poll asks the question, the choices are "approve or disapprove." Neither has a clear middle category.

Both sets of possible responses were designed for journalistic application. Being a journalist usually means having a low tolerance for ambiguity. Politicians and other news sources are always trying to fuzz things up. Journalists are supposed to make things clear. Therefore, it seems natural to frame response categories into discrete, either-or binary choices. But the argument against this forced choice is the same as the argument for inviting "don't knows." Some respondents really belong neither in the pro nor in the con but right in the middle. The Gallup and Harris questions for rating the president's performance were written in a time when most pollsters saw it as their duty to try to force the respondents out of the middle. The current trend is to treat the middle as a legitimate category and include it in the response choices.

Schuman and Presser found that inclusion of the middle does not affect the balance of pro and con, and it does not affect the size of the don't-know category. If some people are most comfortable in the middle, our dedication to truth should compel us to respect that instead of trying to manipulate them into a firmer position. Forcing them out of the middle actually causes us to lose data, because it can mask a real mushiness in attitudes that might be important to know about.9 Although inclusion of the middle alternative need not be an absolute rule, consider it in those cases where you have reason to suspect that the middle represents an important part of reality. On the simplest questions, an invited "don't know" can provide a refuge for the middle. Example: "Should the President send troops to stop the rioting in Xandu, or haven't you thought enough about the situation to say?"

The balanced question

A balanced question presents two alternatives with similar structures. An unbalanced question gives one side and then asks the respondent to agree or disagree (or approve or disapprove). For a complicated issue, the balanced question might take the "some people say" form. Example:

"Some people say the President should be doing more to balance the federal budget. Others say he's done enough already. Which comes closest to your opinion -- that he should be doing more, or that he's done enough already?"

The unbalanced form: "Do you agree or disagree with the following statement: The President should be doing more to balance the federal budget."

Then there is a balanced version with a middle category: "Has the President's action in reducing the national debt been too much, about right, or too little?"

The balanced form is generally better when you are looking for a referendum and your main purpose is to identify a majority or plurality view. However, there are at least two situations where the unbalanced form is justified:

1. Index construction. Some dimensions are too important to be left to one question. You can reduce error by asking a number of questions on the same topic and then combining them into an index. That index can give you a nice continuous measure of whatever you are measuring and provide a check on respondent consistency. An agree-disagree list can generate a lot of index items in a hurry.

2. Creating a simple independent variable. Often the referendum is less important than knowing how one attitude affects another or how an opinion affects a behavior such as voting. In that case, the goal is not to ask an unbiased question but to ask a question in a way that measures the target attribute and splits the population more or less evenly so that you can use it in a cross-tabulation.

In exit polls, which use self-administered questionnaires (SAQs), an agree-disagree list of issues creates a number of variables that can be cross-tabulated against actual vote. In that situation, you don't care about the referendum; you just want to know what issues helped which candidates, what the relative effects of the issues were. To do that, you have to frame the questions to produce binary responses that will divide the population into roughly equal categories.

Here's a rather extreme example. Once in a Florida primary, busing to achieve school desegregation was an issue. We needed an agree-disagree question that would serve as an independent variable in the analysis. Opposition to busing was so strong, however, that it was hardly a variable at all, and so the question had to be loaded in a way that would make it a variable. Agree or disagree: "If the courts require busing to integrate schools, we might as well close the public schools." For that extreme statement, there was enough variance for cross-tabulation.

Response set

Unbalanced questions make poor referenda because of a tendency for some respondents to be "yea-sayers." In a telephone interview an impatient respondent may agree to anything just to get the interview over with. When lists of items are written for possible index construction, the respondent may be more influenced by the form of the question than the content. In psychological testing it is customary to reverse the polarity for alternate questions. For example, an agree-disagree list might include both "The New York Times is fair," and "The New York Times is biased." Some people will agree to both, but at least the yea-saying is compensated for.

Even in the absence of an obvious pattern, response set can cause problems. In 1960 and 1973, different sociologists tried these two agree-disagree statements in the same survey: "It's hardly fair to bring children into the world the way things look for the future." And "Children born today have a wonderful future to look forward to." A disquieting proportion of the people who agreed with the first also agreed with the second.10 Schuman and Presser tried a split-sample experiment where half were asked to agree or disagree with, "Individuals are more to blame than social conditions for crime and lawlessness in this country." The other half was asked for agreement or disagreement to the reverse: "Social conditions are more to blame than individuals for crime and lawlessness in this country." Each version drew a solid majority of agreement.11The maddening thing is that the acquiescence bias, as Schuman and Presser called it, is inconsistent. It doesn't turn up for all issues and questions in a predictable manner.

One situation where you can expect it to cause trouble is when the questions are obviously being used to evaluate something or somebody ñ an institution or a political candidate, for example. If the favorable answer is an agreeing one (or the one on the left in a self-administered questionnaire), the respondent will expect that pattern in the following items and interpret them with that expectation in mind. Reversing the polarity just encourages the respondent to misinterpret the questions; keeping the polarity constant is the safer course.12

Order of response categories

Even when a question is balanced, the order in which the balanced response categories are offered can make a difference. Investigators have found evidence of both primacy effect (favoring the first choice) and recency effect (favoring the last choice). Schuman and Presser report that recency effects are by far the more common. Stanley Payne first noticed recency effects in some split-sample experiments he did for the American Petroleum Institute in the 1940s.13 Schuman and Presser replicated some of those questions more than 30 years later, and the order effects were still there. A sample: "Some people say that we will still have plenty of oil 25 years from now. Others say that at the rate we are using our oil, it will all be used up in about 15 years. Which of these ideas would you guess is most nearly right?" In the 1979 replication, the number believing there was plenty of oil jumped by 13 percentage points when that choice was given last. Schuman and Presser found such order effects in about a third of the items they tested, but they could not discern a pattern that would give a clue to what causes such effects or when to expect them.14

A different kind of order effect can occur when a respondent is asked to judge a series of items in comparison with each other. If you are ever a contestant in a beauty contest, try to avoid being the one judges see first. When USA Today screened television pilots for test audiences in Dallas in advance of the 1989-1990 season, the viewers gave the lowest ratings to the shows they saw first. Anticipating an order effect, USA Today rotated the shows so that they were seen in different order by different groups. Thus "Major Dad" was rated 7.7 on a 10-point scale by a group that saw it before it had viewed any other shows. But a group that saw two other shows first, and therefore had something to compare it to, gave "Major Dad" an 8.8.

Rotation is also a good strategy in a survey interview. If the respondent is asked to rate a list of candidates or a list of issues, reverse the order for half the interviews. Experiments at the University of Chicago, the University of Michigan, and elsewhere have shown that unrelated questions can also be affected by what came before. Something in the content of a previous question can sometimes start a train of thought or set a mood that affects the response to the next. Unfortunately, nobody has found a way to predict these effects. The careful approach, when replicating a question from another survey, as you were advised to do at the start of this chapter, is to look for context that needs replicating as well.

Continuous variables

More information is collected and more sophisticated analysis is feasible if you frame questions with response choices that fit on a continuum. But it is not easy to do, especially on the telephone. In personal interviews or with self-administered questionnaires, you can show a picture of a ladder with the steps numbered from 1 to 10 and ask the respondent to position an attitude on the ladder. Or you can show rows of numbers, 1 through 7, with the ones and sevens headed by words of opposite meaning: biased-unbiased, brave-timid, exciting-boring, honest-deceitful, etc. The odd-numbered scale includes a middle point, and the respondent can mark it with a pencil or point to a card held by the interviewer with relative ease.

Telephone interviewing can do the same if the topic is one that is easily visualized. Using the familiar academic grading scale of A through F is helpful. USA Today in 1989 began using it to get a more sensitive measure of presidential approval. The question: "Using a grading scale of A, B, C, D, and F, where A is 'excellent' and F is 'very poor,' and using any of the grades in between, how would you grade the job George Bush has done as President so far? Would you give him an A, B, C, D, or F?"

Scales of 1 to 10 can also work on the telephone if the subject matter is familiar and the scale is given an explicit anchor. "On a scale of 1 to 10, with 10 being the best possible performance and 1 being the worst possible, how would you rate the President's speech on drugs last night?" Such a question would, of course, be asked only of people who saw or heard the speech.

Yet another way to get some scaling is to loop back after a response to an agree-disagree item and try to split it into strong or not-so-strong agreement or disagreement. But that procedure is time consuming and induces respondent fatigue. You can get away with it on one or two questions, but not with a long list in a telephone interview.

For a key variable, however, it is worth going to some trouble. The National Opinion Research Center question on political party affiliation is a classic. Following a scheme developed at the University of Michigan, it converts the simple Republican-Democrat dichotomy into a continuous variable:

Generally speaking, do you usually think of yourself as a Republican, Democrat, Independent, or what?

(If Republican or Democrat) Would you call yourself a strong (R or D) or not a very strong (R or D)?

(If Independent) Do you think of yourself as closer to the Republican or Democratic party?

The result is a seven-point continuum from strong Republican to strong Democrat. It is a lot of trouble to ask, but worth it if you are studying changes in party loyalty and affiliation over time.

Threatening questions

You can ask about the darndest things in surveys: riot participation, sexual behavior, drug use, all kinds of antisocial behavior. The telephone provides an advantage over the personal interview because you don't have to look at the respondent. And you can create a social situation where it seems natural and easy for the respondent to tell you about his or her bad behavior.

One way is to deliberately load the question to elicit the admission. In the Detroit riot study, the question assumed that everybody was a rioter, and that the interviewer was just asking for details, e.g., "How active were you in the 
riot . . . ?" Then there is the everybody-does-it gambit, reminding the respondent that the behavior asked about is fairly common. "A lot of people yell at their spouses some of the time. Did your spouse do anything in the last seven days to make you yell at (him/her)?" That wording also suggests that your yelling was your spouse's fault, not yours.

Students in my advanced reporting class measured cocaine use in Orange County, North Carolina, by asking a graded series of questions on substance use, starting with tobacco, beer, and wine and working up through hard liquor, amphetamines or tranquilizers (uppers or downers), marijuana, and finally cocaine and heroin. The early questions about legal drugs set up a pattern of disclosure that could be maintained when the illegal drugs were asked about.

For a survey to determine the incidence of date rape, another class used the telephone to recruit respondents who would agree to fill out and return a mailed SAQ. They were warned in advance that it contained some sexually explicit questions. In addition to asking directly about rape, the questions asked about more detailed behavior, including one that amounted to the legal definition of rape: "Have you ever had sexual intercourse with a woman without her consent?" Like the cocaine question, this came at the end of a series asking about more benign behaviors. Far more males admitted to nonconsensual intercourse than would admit to rape when the word itself was used, which raised some interesting issues about the social definition of rape.

Questions about prosocial behavior can be threatening if the respondent failed to perform the approved action. To coax out admissions of nonperformance, it helps to build some excuse into the question. "Did you happen to vote in the last election, or did something come up to keep you from voting?" Even with that wording, past voting is generally overreported. "Do you use a seat belt when you drive, or are you one of those people who hate being strapped down?" can encourage an admission of nonperformance. Even better would be asking about a specific time, i.e., "the last time you drove." That way the person could admit to not performing a desirable behavior just once without seeming to be a total nonperformer. The Newspaper Advertising Bureau question on newspaper readership asks if the respondent read "yesterday" for the same reason.


Every ongoing polling operation should have a standard list of demographic categories and stick to it. Making comparisons across time is an important way of enriching your data, and you need consistent categories to do it. Here are the demographics you should collect as a minimum:

1. Gender. Two categories will do.

2. Race. Find out whether the respondent is black, white, or something else. The something else could include Asian or Native American, but not Hispanic. The designation of Hispanic refers to national origin, not race, and there are in fact Hispanics who are white, black, Asian, and Indian. So ask about Hispanic origin as a separate question, after you have asked about race.

3. Age. Ask for exact age. You can set the categories in the analysis. It is important to maintain flexibility here, because the relevant age categories can depend strongly on the news topic. There is a myth among pollsters that asking for exact age irritates the respondent and ruins cooperation. When USA Today switched from asking age category to asking exact age, the refusal rate went from .33 percent to 1.5 percent -- an increase of nine refusals in an 800-person survey. That's not too much to pay for the ability to set whatever cutting points the analysis requires.15

4. Education. Asking for exact number of years in school preserves your flexibility. But the categories you will usually end up with are these: grade school (0-8), some high school (9-11), high school graduate (12), some college (13-15), college graduate (16), and post-graduate (17+). In North Carolina, some older people got high school diplomas with only 11 years of school, so a more detailed question has to be asked.

5. Income. This one is usually saved for last, because the refusal rate is relatively high. Because of inflation, it is impossible to set categories that will make a lot of sense over time. A common format is to have the interviewer read a list of categories after having asked the respondent to "stop me when I get to your category." Usually, total household income before taxes, not the respondent's own income, is requested. Experiments with this question have shown that the more different kinds of income are asked about, the more income surfaces. For many newspaper surveys, however, education is enough of an indicator of socioeconomic status so that income is not needed unless it is particularly relevant to the story, e.g., one on tax policy.

6. Religion. The common categories are Protestant, Catholic, Jewish, and None. In the parts of the South where half the population is Baptist, the Protestants can be subdivided into Baptist and other.

7. Work. To see how complicated occupation codes can get, check the codebook for the General Social Survey.16It would be nice if you could sort people into blue collar, white collar, and professional categories, but too many jobs are hard to classify. You can, however, ask whether a person is working, unemployed, retired, keeping house, or going to school.

8. Marital status. Married, never-married, widowed, divorced, separated.

9. Region of socialization. Sometimes the kind of place in which a person grew up is relevant to your story. For consistency, consider using the regions of the United States as defined by the Bureau of the Census. You'll find them on the inside cover of the Statistical Abstract of the United States.17

Size of place

Don't ask this one. Just code it from what you already know about the respondent's city, county, or zip code. A useful distinction is between urban and nonurban, defined as counties that are part of Metropolitan Statistical Area and those that are not. Even a state with no large cities, such as North Carolina, can end up with a neat half-and-half division on that dimension.


Data are collected in person, by mail, and by telephone. Technology keeps bringing new methods. Both personal and telephone interviews can be assisted by a computer that stores both the questions and the answers. A market research firm in The Netherlands has even automated the self-administered questionnaire. Each Friday, members of a previously selected sample turn on their home computers and dial up a central computer system that then asks them questions about their attitudes and their week's purchases. It is a reverse database, and the respondents are motivated to be a part of it because they get a free computer. For the foreseeable future, however, you will have to cope with live interviewers most of the time.

Training interviewers

Whether interviewing is done in person or by telephone, the interviewer must know both the elements of social science data collection and the specific aims and characteristics of the study at hand. A survey interview is a conversation, but it is an unnatural conversation. As any reporter knows, you could take the respondent to the corner bar, spend some time over a couple of beers, and get a better idea of the person's attitudes.18 Such a conversation would generate insight, but not data. To produce quantifiable data, you have to train individual differences out of the interviewer so that the questions will produce the same responses no matter who is asking them. The technical term for this consistency is reliability. Achieving it may come at some cost to validity or the essential truth of the answers you get. But without reliability, you can't add one interviewer's apples to another's oranges. So you train the interviewers to behave in uniform ways that squeeze the subjectivity out of the process.

The interviewers have to be taught to read the questions exactly as read. If the question as read does not yield an answer, the interviewer is allowed to use neutral probes, e.g., "un-hunh," "Could you be a little more specific?" or just an expectant pause. Suggesting a response is not allowed. "You mean you approve of the way President Bush is doing his job?" is not a neutral probe.

Interviewers are allowed some freedom, however, in the introductory part of the interview. You will write a script for them that opens the conversation and requests the respondent's cooperation, and it is okay for the interviewer to use it with improvisation. But when the data collection part begins, he or she must stick to the script.

Some of the questions from potential respondents can be anticipated: who is paying for this survey, will my name be published, etc. It is a good idea to make a list of the expected questions and recommended answer for each interviewer to have at hand during the data collection. For some excellent examples of the written instructions with which interviews can be fortified, see Don A. Dillman's book Mail and Telephone Surveys: The Total Design Method.19

Help your interviewer trainees to become familiar with the questionnaire by role playing. Pick one to interview another in front of the group. Then do it again with you acting as a particularly difficult respondent.

Reassure your trainees that most people enjoy being interviewed. It is not necessary to act like a detective on a secret mission. If yours is a prestigious media company, mentioning its name in the opening pitch will help convey the feeling that a good cause is being served by participation.

CATI systems v. paper and pencil

If you have the resources, a CATI system saves time and improves accuracy. Computer Assisted Telephone Interviewing requires a personal computer or a mainframe terminal at each station. In its simplest form, you program the questions on a floppy disk and make a copy for each interviewer. The questions appear on the screen, and the interviewer punches the answers into the computer and they are written on to the same floppy disk. At the end of the evening, the disks are collected and the data compiled on a master disk. If your personal computers are part of a network, the answers can be directed to the file server as they are collected, and you can make running frequency counts. Some mainframe and networked systems even allow for questionnaires to be revised on-line to respond to news events that break while a survey is in progress.

If you use pencil and paper, design the form so that the answers are recorded on a separate sheet of paper. A vertical format makes data entry easier. I prefer a three-column answer sheet with the response spaces matched horizontally to the question sheets. That reduces printing costs because you need one questionnaire per interviewer, not one per respondent. And the answers will usually fit on one piece of paper, front and back, which eliminates a lot of tiresome page turning during data entry.

Before finalizing a questionnaire and answer sheet, show the answer sheet to the person who will be responsible for data entry to make certain that it is workable. When data were entered on punched cards, it was standard practice to precode the answer sheets so that the eventual column location of each item was indicated from the start. Now that data entry folks work with direct computer input, that is not as necessary. But check it out anyway to make sure you have not left any ambiguities.

Calling back

You will need to develop a paper trail to keep track of each interview attempted. The more advanced CATI systems can do most of this work for you and even manage the sample. Otherwise, you will have to keep interview attempts sorted into these categories: 

1. Completions.
2. Appointments to call back the next-birthday person.
3. Busy signals and non-answers that need to be tried again.

4. Refusals, nonworking numbers, business numbers, and other outcomes for which substituting a number is allowed.

Naturally, you will want to keep track of all of these outcomes so that you can spot inefficiencies in your operation and work to improve it.
How many times should you attempt a non-answer before you give up? Three times on different days and at different times of day would be good. Journalists working under deadline pressure can't always manage that. The Carolina Poll, conducted by the students at the University of North Carolina at Chapel Hill, uses three call-backs spaced a minimum of an hour apart. That usually forces one of the attempts to another day. Even then, cleaning up the last few cases can be messy, and the Carolina Poll sometimes switches to a quota sampling method for the last 10 or 20 percent of a project.


Quota sampling

Quota sampling got a bad name when it was blamed for the bad election forecasts made by all of the major polls in 1948. In fact, other mistakes contributed to that spectacular error as well. Quota sampling still lives on in a less dangerous form when it is used in combination with probability sampling for the last stage of respondent selection.

Probability sampling is used to choose a cluster: a cluster of homes or blocks in a personal interview sample, or a cluster of telephone numbers from a single NNX in a telephone sample. In its loosest form, the quota sampling method allows the interviewer to select whoever is most readily available from then on, subject to loose age and sex quotas. A simple way of setting the quota is to instruct the interviewer to speak to the youngest male at each household. That compensates for the relative difficulty of finding young people and males at home. If there is no young male present, the interviewer asks for the youngest female.

In a slightly more rigorous form, call-backs are still made at the household level to reduce the bias from non-answers and busy numbers, but the sample frame is limited to whoever is at home once the phone is answered. Again, the youngest male is asked for (females are more likely to answer the phone).

Sometimes the pressures of the news will limit the time available for fieldwork to a single night, in which case quota sampling at the household level and instant replacement of unavailable households will be necessary. The bias in favor of people who are easy to find may or may not be important. For political topics, it often is. If you notice instability in a series of competing pre-election polls, try dropping the one-nighters from the comparison and see if what is left looks more consistent.

Collecting data by mail

Mail surveys are slow. And they can be surprisingly expensive. You have to do more than get a list, write a questionnaire, send it out, and then wait for the postman to bring you the results.

If you are thinking of doing one, put this book down and get the Dillman book cited earlier. Dillman's advice comes in exquisite detail, down to what kind of envelope to use and how to fold the questionnaire. (Some of his advice needs updating. For example, he says it is okay to use a business-reply frank. Later research has shown that a live stamp on the return envelope gets a better return. Apparently, potential respondents hate to waste the stamp.)

A mail survey should not be too short. It will seem trivial and not worth bothering about. If too long, it will seem like too much work. One sheet of 11 by 17-inch paper, folded to make four letter-size pages, is about right. Enclose a come-on letter and a stamped, addressed return envelope. Mark the letter or the questionnaire with a visible code so you will know who has responded and explain the purpose of the code ñ along with any assurances of confidentiality you want to give ñ in the come-on letter. At the same time you prepare this material, prepare a reminder postcard, to be sent five days after the original mailing without waiting to see who responds unprompted. (Naturally, the card will include some apologetic language: i.e., "If you have already responded, please accept our thanks.") After two weeks, send a reminder letter with a fresh copy of the questionnaire to the nonrespondents. 

A personal computer database program like Paradox or PC-File is useful for managing the mailing list and keeping track of the returns. The trick is to get a healthy response rate of two-thirds or better. So always choose a sample small enough to leave the time and resources for vigorous follow-up -- including pleading phone calls if necessary ñto motivate nonrespondents.

Mixed-mode surveys

Mixing mail and telephone methods works well when you need to show an exhibit for the respondent to judge: a sample product, a newspaper layout, or a photograph of a blooming celebrity, for example. You can use random digit dialing sampling for the initial contact, get the respondent to agree to accept the mailing, and then call him or her back to ask questions about the mailed material. Again, a personal computer is helpful in keeping track of who has agreed to do what.

The USA Today 1989 fall television evaluation project was a mixed-mode survey in that the telephone was used for the initial contact and for asking questions to identify frequent TV viewers. Then respondents who met the criteria were offered $40 to come to a central location to watch the shows and evaluate them on a self-administered questionnaire. The respondents had already passed one level of telephone screening. The research supplier who recruited them maintained a list of thousands of people who had expressed interest in evaluating products. With such a group it is sometimes difficult to know to whom you can generalize: heavy TV viewers in Dallas who are interested in product evaluation and could use $40 and aren't doing anything else that night. There is no problem with doing that so long as readers are advised and no pretense is made that the group represents the nation's TV watchers as a whole. They are still likely to be more representative than your average jaded newspaper TV critic.

Make-or-buy decisions

Any news organization that does a lot of polling sooner or latter has to make what business schools call the make-or-buy decision. Is it better to farm the polling out to a firm that specializes in the work, or to do it yourself?

The important thing to recognize about this decision is that it is not all or nothing. Different pieces of a project can be separated and done in-house or sent out. The general rule to remember is this: 

Doing work in-house hides costs and reveals inefficiencies. Work sent out has visible costs and hidden inefficiency.
Sampling is a piece of a survey project that is easily severable. So is the fieldwork. You give the supplier a sample and a questionnaire and he or she gives you back a stack of completed questionnaires. Putting the questionnaires into computer-readable form is readily farmed out to a data entry specialist. Analysis is not so readily delegated. That is really a journalistic function and something that should be done by the news organization's own people.
Doing it all yourself may look cheap, but that's because you aren't counting the whole cost: overhead for your plant and equipment, for example, and the salaries of all the people in your organization who will help you. The main reason for doing it yourself is to maintain control, to restrict the journalistic functions to journalists. Survey research is a powerful tool, and a news organization can keep it under control by keeping it in-house.



1. Dennis Trewin and Geof Lee, "International Comparisons of Telephone Coverage," in Robert Groves et al. (eds.), Telephone Survey Methodology (New York: John Wiley & Sons, 1988), pp. 9-24. return to text

2. Leslie Kish, Survey Sampling (New York: John Wiley, 1965).  return to text

3. This rule and the correction factor come from Hubert M. Blalock, Social Statistics (New York: McGraw-Hill, 1960), p. 396.  return to text

4. Described in Philip Meyer, Precision Journalism, Second Edition (Bloomington: Indiana University Press, 1979), p. 306. return to text

5. Howard Schuman, Ordinary Questions, Survey Questions, and Policy Questions," Public Opinion Quarterly, 50:3 (Fall 1986), 437.  return to text

6. Philip E. Converse, "Attitudes and Non-Attitudes: Continuation of a Dialogue," 17th International Congress of Psychology, 1973.  return to text

7. Howard Schuman and Stanley Presser, Questions and Answers in Attitude Surveys: Experiments on Question Form, Wording and Content (New York: Academic Press, 1981).  return to text

8. Cited in John P. Robinson and Robert Meadow, Polls Apart: A Call for Consistency in Surveys of Public Opinions on World Issues (Cabin John, Md.: Seven Locks Press, 1982).  return to text

9. Some distinguished social scientists agree with me. See Seymour Sudman and Norman M. Bradburn, Asking Questions: A Practical Guide to Questionnaire Design (San Francisco: Jossey-Bass, 1982), p. 141.  return to text

10. Cited in Robinson and Meadow, Polls Apart, p. 124.  return to text

11. Schuman and Presser, Questions and Answers in Attitude Surveys, p. 208.  return to text

12. Philip Meyer, "Defining and Measuring Credibility of Newspapers: Developing an Index," Journalism Quarterly, 65:3 (Fall 1988).  return to text

13. Stanley Payne, The Art of Asking Questions (Princeton: Princeton University Press, 1951), p. 133.  return to text

14. Schuman and Presser, Questions and Answers in Atitude Surveys, p. 72.  return to text

15. Calculated by Jim Norman on the basis of 17 surveys by Gordon Black for USA Todayreturn to text

16. General Social Survey, Cumulative Codebook (Roper Center, University of Connecticut), updated annually.  return to text

17. Statistical Abstract of the United States (Washington: U.S. Government Printing Office). Published annually. return to text

18. The beer-hall analogy was used by Elizabeth Noelle-Neumann, "The Public Opinion Research Correspondent," Public Opinion Quarterly, 44:4 (Winter 1980), 591.  return to text

19. (New York: John Wiley & Sons, 1978), pp. 260-267.  return to text

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Index button

Chapter 1 - Journalism and the scientific tradition
Chapter 2 - Some elements of data analysis

Chapter 3 - Harnessing the power of statistics

Chapter 4 - Computers

Chapter 5 - Surveys

Chapter 6 - More about data analysis

Chapter 7 - Field experiments

Chapter 8 - Databases

Chapter 9 - How to do an election survey

Chapter 10 - The politics of precision journalism