Everyday Sociology Blog

What's the Difference between Sociology and Journalism?

By Karen Sternheimer

It’s not uncommon for students to ask me this question, particularly after reading a selection from ethnographic research. In my opinion, good journalism and good sociology have a lot in common, but there are important distinctions. Some excellent sociological work is actually done by journalists— Barbara Ehrenreich comes to mind—and journalists occasionally use sociologists as sources for analysis or for context for their stories.

The following points are not exhaustive, nor are they intended to be a set of rules, but they do provide a general guide to the distinctions between sociology and journalism.

1. In journalism, time is of the essence

One of the purposes of journalism is to let us know what happened that day, or increasingly what is happening right now. Sociology has the luxury of time: if you ever noticed, research published in journals was typically conducted at least a year earlier. And if a study is based on a large data set, say from the census or another government agency, it is likely to be at least two to three years old.

This does not mean that research is necessarily outdated; sociology is about analysis and reflection, for which we need time. Journalism often includes analysis, but rarely are stories reflected on years later unless they are major events, like the attacks of September 11^th or the aftermath of hurricane Katrina.

2. In sociology, data collection is systematic and grounded in theory

Have you ever seen a news segment where they ask passersby on the street about an event of the day to try and grasp public opinion? While this might give the appearance of a random sample, it certainly is not. What street are the reporters standing on, and when? What kinds of people might be available for interviewing, and who might not ever go into that area? Who is willing to appear on camera, and who is not?

When I have taught research methods we talk about this, and there are always a few people who insist that this is as good a way as any to find out what “average” people are thinking. While such on-the-street interviews might add some color to a story, sociologists tend to employ more rigorous sampling methods. 

In one study I worked on years ago, we purchased the customer list from a utility company and selected every fourth household to participate in the study. Is this a perfect, fool-proof method? Of course not. Some people might have had their gas or electricity cut off, and as we found out, the list provided addresses that did not exist.

Conducting ethnographic research is a lot like in-depth reporting, although sociologists tend to spend much longer with a group that journalists might (although this is not always the case). Journalists spending time with a group in their everyday environment might also incorporate information about the broader context, but sometimes the purpose is just to better understand what it is like to be in their shoes. 

This is true of sociological research too, but more often than not sociologists attempt to connect their findings to a theory, or sometimes they create one of their own based on their findings. Yes, journalists might bring in theories too, but this is less common than in sociology.

3. Journalists’ work is aimed at a wide audience

Just as I often think journalists can include more contextual information in reporting, sociologists can learn a lot from how journalists present their findings. Journalists do focus on different audiences and thus go into varying levels of depth; for instance, on television the local news has a different target than Charlie Rose, and in print the Wall Street Journal’s coverage varies from USA Today’s. If you have read the two newspapers you can spot the difference right away. Even if journalists focus on a niche audience, it is almost always larger than the audience sociologists tend to write for. 

Monte Bute wrote a very provocative review in Contexts, a sociology magazine that aims to reach a larger audience than just sociologists. He describes a study that found in recent decades, articles published in major sociology journals have become more jargon-filled and less accessible to general audiences. The attempt to solidify sociology’s stature in social science has had one major downside: it makes for dry reading and therefore has a limited audience.

I know for some people, and perhaps for some research, this is just fine. But sociological thinking should not be out of reach, and should instead become more available to the public. One way to do this is to promote writing that is engaging and informative without being overly superficial. It takes years to unlearn the tendency to write in the passive voice, to remove all traces of the author’s voice and use enough jargon to prove that you know the lingo. Ethnographers tend to also write well, as if losing the pretense of total objectivity gives one license to tell a good story.

Journalists and sociologists have a lot to learn from one and other. Hopefully in the future journalists will make greater use of sociological research and thinking in their reporting and will also provide their readers with a greater understanding of the general context that informs their stories. For this to happen, sociologists have to broaden our scope and target our findings more generally, and yes, we also need to become better at communicating our ideas. Both journalists and sociologists study who, what, where, when and why; it’s the “how” where we tend to differ.

Statistics and Myths about Immigrants

By Sally Raskoff

A friend sent me an e-mail that I found very alarming. Although I consider this person a friend, we have never really talked about politics. But I was still surprised when the missive below came from her.

Her email was obviously a chain letter expressing frustration about California’s problems, allegedly due to illegal immigrants. The  content of the “evidence” is supposed to be from the Los Angeles Times and lists many statistics that lay blame for scary and negative situations squarely upon illegal immigrants.

As a sociologist who teaches statistics, I could not let this go without a response. While I included in my email response a tactful discussion of the reasons why these statistics are problematic, I’d like to invite you to help identify what the problems are with this message.

I’ll start with the source – stating that these came from the Times isn’t sufficient to give them credibility. No date, page, research source or author is mentioned. These could have come from an advertisement in that newspaper or, more likely, never appeared there to begin with. Searching the LA Times online, even with quotes from the text, no connections appear.

Many of the statistics are illogical: “95% of murder warrants … are for illegal ‘aliens’”? The 95% is a big red flag. Few human patterns, especially crime patterns, are so simple that there can be an easy explanation.

Other statistics mentioned are more about prejudice than serious social problems, like this one: “21 radio stations in LA are Spanish speaking”. 

Since these statistics are all about Los Angeles and California, the research reported by the Public Policy Institute of California provides a good contrast to these figures. In their June 2008 “Just the Facts” report on “Immigrants in California,” they state that “Immigration has directly accounted for 40% of the state’s population growth since 2000,” which is a figure much less than the e-mail’s purported 90%. 

Finally, checking the text of the email on snopes.com (a site devoted to investigating hoaxes) this message has quite a history as it has been circulating since 2006. 

Questioning those email forwards and considering the accuracy and source of information that comes our way are crucial steps in critical thinking and forging a pathway based on accuracy rather than ignorance. Do you see any other problems in this email snippet? How would you go about finding unbiased and accurate sources to check this information?

 

(Photo courtesy of the National Archives www.nara.gov)

Types of Causality, III

By Bradley Wright

It’s the end of summer, the beaches are hot, friends are home, and you’re using up what little vacation time you have left. What better thing to do than think about causality? I know that I am!

Today I’m writing about two more concepts associated with causality. The overriding point of this series is to help you think through the multi-faceted nature of causality. Everyday we use causal language—this happened because of that—but we’re not always clear in our thinking about what is, and is not, a cause. So, on to more types of causation:

Interaction effects. An interaction effect occurs when the effect of one variable on another varies by levels of a third variable. Let’s say that we’re looking at how levels of variable “A” affects variable “B”, but we find out the effect of “A” varies by how much of “C” we have. If so, that’s an interaction effect.

Interaction effects are perhaps best described by example.

Would drinking a beer or two or three make you feel light-headed? Well, it depends on various factors, including what you’ve eaten. If you have just had a full meal, the food in your stomach slows down the absorption of alcohol which in turn means it has less effect on you. However, if you’re drinking on an empty stomach, then the alcohol will have a much greater effect. This is an interaction effect. The effect of alcohol (A) on how you feel (B) depends on what you’ve eaten (C). We could think of other “C” variables as well, variables that alter the effect of alcohol on you. It also varies by your body weight, how much you’re used to drinking, your gender, and your metabolism.

Here’s another example of an interaction effect. I like watching late-night talk shows, and I find the monologues to be really funny. Recently, however, I’ve tried watching them during the day on the shows’ websites, and I didn’t enjoy them at all. The same joke, told late at night, will have me in stitches but in the middle of the day bores me. To really enjoy them I think that I have to be tired and not planning to do anything else. So, the effect of late-night jokes (A) on my laughing (B) varies by the time of day (C).

Causation & correlation. A statistical truism is that correlation does not necessarily imply causation. This reflects the fact that two variables can be spuriously correlated.

Just for laughs and giggles, though, let me point out that the opposite is true as well. Causation does not necessarily imply correlation. That is, some variable “A” can be a cause of “B” without actually being correlated with it. I know what you’re thinking—this must be some kind of sociological magic, but, really, it’s true. Here’s how it works.

The cause, “A”, might increase “B” through some mechanisms, say “C.” It might also decrease these “B” through other mechanisms, say “D”. As such, “A” has a causal impact on “B” even though there is no association between levels of “A” and “B” in a population of people.

Suppose that you ate a dark-chocolate candy bar. One of those 65% cocoa ones—you know what I’m talking about. Now, the other 35% is probably sugar and butter, which have a lot of calories which should increase you’re weight. But, the caffeine in the bar might give you enough energy so that you bounce around the house for awhile, thus burning off the calories. As such, eating dark chocolate has a causal linkage to weight gain but is not correlated with gaining weight. 

I actually used this principle in some research I conducted. For some time, criminologists have had theories about why poor people commit more crime. They have more strain put on them, fewer opportunities for conventional work, etc…. However, self-reported studies found little correlation between social class and crime. Why? Turns out that there are mechanisms though which being poor lowers crime rates, or, put more intuitively, being wealthy promotes crime. The wealthy are more likely to think that they can get away with crime, so they are less deterred by possible punishments. They also foster an ethos of risk-taking, which associates with more crime.

So, social class both increases and decreases criminal behavior, and we have causation without correlation. Cool, huh? 

Okay, back to your regular summertime activities.

Types of Causality II

By Bradley Wright

This post continues a several-part series on causality. Now, why would I spend so much time writing about causality? Maybe I have too much time on my hands, or maybe I think that the readers of this blog have too much time? Actually, the issue of causality comes up in a lot of social research. When a study presents some findings about the social world, these findings usually make a whole string of assumptions about causality. As such, to understand fully sociological research, we need to have a working knowledge of the basics of causality—hence my addressing the topic in this series.

In this post I continue a discussion of different types or dimensions of causality.

Let’s consider linear vs. non-linear causality. Sociologists like to be boring when we talk about methods, so we usually end up using letters in our examples rather than real things. For example, we talk about “x” being a cause of “y.” Far be it from me to violate this norm, so I will use letters, but I’ll also try to give examples to make the ideas more clear.

Linear causality happens when every increase in “x” prompts a similar change in “y”, regardless of the value of “x.” Every time you change “x”, you get the same change in “y.” For example, my sons like to take allowance money to the store and buy a bag of candy. The candy is about a dollar, so every dollar they spend (and, thankfully, it’s usually only one or two), they get a bag of candy. If they spend one dollar, they get one bag. If they spend $50, they get fifty bags. This is a linear relationship—no matter what the value of “x”, whether $1 or $50, a one-unit change in “x” produces the same change in “y”—one more dollar equals one more bag.

In contrast, non-linear causality happens when the effect of “x” on “y” varies by levels of “x”. There are countless forms of non-linearity, but commonly maybe “x” brings about some change in “y” at low levels of “x”, but it causes much less change in “y” at high levels of “y”. 

Here’s a simple example. I like bicycle riding—and even have one of those fancy recumbent bicycles—but I don’t do much riding during winter because, well, it’s cold and snowy out. So, when I first go out in spring, I average about 12 miles an hour. (The area around here is all hills, so average speeds are lower than if it were a flat part of the country). After a month of riding, however, this increases to 14 miles an hour. So, one month riding = two miles an hour extra. If training and speed were linearly related, every additional month would always produce an extra two-miles-an-hour. Let’s see, after six months of the riding season, I’d be at 26 miles an hour—Tour-De-France speed. If I could ride steadily for another year or two, I could be passing cars on the interstate. Alas, that’s not the way it works, for training is a cause, but a non-linear cause, of riding speed.

Now let’s think about unidirectional vs. reciprocal causation. So far, I’ve discussed one variable as a cause and another as the effect. This is unidirectional causation, that “x” causes “y”, but “y” doesn’t cause “x”. Unidirectional causation is usually what people talk about when they talk about causation. However, there is also reciprocal causation. Here, “x” causes “y”, as in unidirectional causation, but also “y” causes “x” at the same time. They are both causes and both effects—how cool is that!

As I mentioned , New England is all hills. We also have rocks, not just a few—a lot. If you dig a hole to plant some flowers, you probably end up tossing out several rocks. (As an aside, the rocky terrain has shaped New England’s social and economic development in numerous ways, but that’s for another time). What do we do with all these rocks? Well, sometimes we build walls out of them. Pretty much wherever you go around here, you see rock walls. The farmers of previous centuries would drag the rocks out of their fields and put them on the borders of their properties, thus both clearing their fields and marking their territory. Homeowners have kept with that style, and they often put up rock walls as part of the landscaping. For me, building rock walls is a summertime hobby, and I’m on my third one. When I put a rock down as part of a wall, it pretty much stays there and doesn’t do anything back to me. This would be an example unidirectional causation—my efforts change the location of the rock.

But let’s say that I went bad, and instead of building walls, I picked up the rocks and started throwing them at my neighbors. Some of my neighbors are elderly widows, and they don’t put up with any crap (plus I think they have pretty good throwing arms), so they would probably start throwing the rocks back at me. As a result, my throwing would give them bumps on the head, but, in response to me, they would throw the rocks back and give me bumps on the head. This is an example of reciprocal causation—the bumps on their heads are both effects of my throwing and causes of their throwing.

Who would have thought that causation could be so dangerous?

An Effect of Measurement

By Bradley Wright

A friend of mine bought a hybrid car—one of those cars that runs on both gas and a battery engine—and when he gave me a ride, I was struck by two things. First, the car is amazingly quiet when it is running on battery power; in fact, I wouldn’t have even known that the engine was running except we were driving down the street. This, I thought, would be perfect for sneaking up on pedestrians (but that’s a topic for another blog post). Second, my friend, who is otherwise quite sensible, spoke at great length about the gas mileage that he gets with the car, and how it varies by driving patterns and terrain. Apparently braking slowly is good (or bad) because it does (or doesn’t) charge the battery. (You can tell I wasn’t paying too much attention).

Now, I thought my friend was unusual in his fascination with miles-per-gallon until I read this Washington Post article about hybrid owners. It tells of various owners who seem willing to do anything for that extra mile-per-gallon. One driver changed his route to work, just to avoid a big hill that drops his mileage to below 20 mpg. Another is so keen on keeping his mpg high that he won’t let his wife—who apparently just drives normally—drive his hybrid.

The key feature of hybrids that makes this mpg obsession possible is a dashboard mileage monitor that indicates how many miles-per-gallon the car is getting at that exact moment. This feedback appears to change drivers’ behavior.

Sociologists have long understood what is called an observer effect (also called the Hawthorn Effect). The idea here is that people change their behavior when they know they are being observed. This is why sociologists will sometimes use covert observation to study a situation, so as not to change it unnecessarily. It’s also why experimenters will often deceive their subjects about the true purpose of the study.

Well, related to the observer effect might be something that we can a measurement effect. Just the act of measuring a behavior changes it to be (usually) more in-line with our preferences and goals.

This principle applies to much more than driving hybrid cars. In fact, when people want to change their own behavior or that of others, one of the first things they’ll do is start measuring that behavior. It’s remarkable in how many areas of life we use this measure-to-change-it approach to behavior.

Weight Watchers is one of the best known weight loss programs. What’s one of the first things that a person does at the Weight Watcher’s meeting? Step on a scale, and have someone write down how much you weigh. This measurement brings your attention to what you’re trying to do, and it indicates how well you’ve done it in the previous week.

Most money management programs operate on the same principle. They have you keep track of all your expenses (i.e., measure them), and then see how they change over time. (I tried a program, called Money Counts, and I realized that I’m better at sociology than managing money.)

Want to live a more holy life? Start confessing. The Catholic Church encourages its members to periodically tally up their sins for a priest who then (hopefully) absolves them of these sins. The awareness of sin that the periodic confessions encourage should help move the individual away from behaviors they don’t desire.

Fundraising programs not only ask for money, but they also let their target audience know how much they’ve already raised. This is why every summer we see signs with thermometers painted on them. The more money given, the higher the red-mark on the thermometer.

I suppose that even classroom grading works this way. Students who know their grades throughout their semester probably study harder and are more engaged in the tests than those who are not told their grades. (This is why professors always tell students their grades.)

This principle has implications for social research. Just the act of measuring someone’s behavior, e.g., as is done in a survey, can change that person’s behavior by making them more aware of what they are doing. This may not matter in a cross-sectional survey, done only once, but with longitudinal research, it may alter the data. That is, if we measure a person’s behavior a second time, we may observe something different than if we hadn’t measured it a first time.

What’s the bottom line here? Well, when we measure anything, whether in professional research or everyday-life, realize that we’re probably changing some aspect of it. If we want to change something, probably the first thing to do is to start measuring it.

Types of Causality

By Bradley Wright

(Part II of a series)

In my last post, I wrote of basic ideas about causality. Sociologists most readily assume that one thing in the social world causes another when the cause correlates with the effect, occurs before it, and there’s a plausible, non-spurious causal mechanism. 

In this post, I develop the idea of causality more by talking about its different types. Yes, just as there are different types of cars, ice cream, and television reality shows, there are different types of causality. Knowing these distinctions in causality allow us to recognize causality more readily and to think about it more deeply… and you thought you’d have nothing to do during your summer vacation!

1) Nomothetic vs. Idiographic . The first distinction involves two words no one has ever heard of: nomothetic and idiographic (they come from the Latin phrase “really confusing”). They regard how many cases are being explained—many or just one. 

Nomothetic means a causal relationship is assumed to happen among many cases. (Sociologists usually study people as cases, but these principles apply to non-people cases like bears or stars or battles). With nomothetic causation, some cause has some effect on lots of people. This is what sociologists do almost all the time, and it’s so routine, we can forget that there is anything else.

Idiographic causation, however, involves just one person (or case). Basically, you’re saying that a cause has an effect for one person or thing, and you don’t know, or don’t care, whether it affects others. 

Now, why would anyone want to go idiographic? Well… historians might want to explain why a specific event happened. For example, why did the American Civil War occur? Different historians might have different explanations, but they are not trying to explain all wars (this would be nomothetic) or even all civil wars (again, nomothetic), but rather why this particular war happened.

2) Deterministic vs. Probabilistic . The next distinction of causality is fortunately easier to pronounce, but it still identifies a type of causality that people sometimes miss. This distinction regards whether a cause happens every single time or just some of the time.

Deterministic causation occurs when every time you have a cause, you have an effect. For example, every time you cool pure water to 32 degrees, it freezes into ice. (Okay, I suppose any science types reading this would find an exception, e.g., maybe water under high levels of pressure doesn’t freeze or maybe it acts differently in outer space, but work with me here).

Sociologists rarely, if ever deal with deterministic causation. Instead, we’re all about probabilistic causation. This happens when a cause sometimes brings about an effect, but not always. A classic example is smoking and lung cancer. Not everyone who smokes gets lung cancer, but it definitely increases the likelihood of it, so we call smoking a cause of lung cancer.

Sometimes people confuse this distinction, and they proclaim that if a cause is not deterministic, it is not really a cause. For example, someone might argue that since their grandfather smoked a pack a day and died at natural causes at 100 years old smoking doesn’t cause lung cancer.

In this sense, probabilistic causation is more difficult to deal with (and I think more interesting) because it does have lots of exceptions, but, it’s still causation. 

3) Necessary vs. sufficient. This next distinction is rather tricky. It’s the one that that most sociology students have to stop and really think about when they’re answering a midterm question. It regards whether a particular cause is needed to bring about an effect and if that cause is enough by itself.

Necessary causation occurs when you absolutely, positively must have a certain cause to observe a corresponding outcome. Simply having that cause may not be enough to have the effect, but you definitely need that specific cause to have any hope 

of observing the effect. A simple example: You need air to be alive, so air is a necessary cause for life. Air, by itself, might not be enough, for other things can come into play. Maybe you were just bitten by some crazed monkey that has Ebola, and you’re going to be dead by sundown no matter how much air you breathe.

Sufficient causation means that a particular cause is enough to have an effect, though other causes could bring that effect about as well. Eating a pint of Ben & Jerry’s ice cream every day is enough to make you fat just by itself (assuming that the rest of your diet stays the same). It is a sufficient cause of weight gain. (By the way, I just tried their flavor “Visual Whirled Peace” today—outstanding). But, there are also other possible causes of weight gain, such as Haagen Dazs and Bryers ice creams.

Just to keep you on the edge of your computer chair, let me tell you that I plan to write about several other distinctions in causality in my next post. How sweet it is…

Causation

By Bradley Wright

Sociologists spend a lot of time talking and thinking about causality. (We probably spend even more time with office politics, but that’s not very interesting, so I won’t write about that).

Now, I have a sneaking suspicion that philosophers spend a lot of time defining exactly what is the essence of causality, and they probably trace it back to Romans and Greeks and people like that. Rather than go into this type of philosophical analysis, I will simply focus on how we might test for its existence.

If “A” is a cause of “B”, what does that mean? In this case, “A” and “B” could be just about anything—characteristics of people, interactions, groups, societies.

First off, we would expect some level of association between “A” and “B”. By this we mean that as levels in “A” change, we would expect usually to see some change in “B”. Some associations are positive, meaning that “A” and “B” move in the same direction. So, as “A” increases, “B” does also. (Or, conversely, if “A” decreases, so does “B”). Other associations are negative, meaning that as “A” increases, “B” decreases or the reverse.

Second, we should see changes in “A” occur before changes in “B”. Since very few sociologists can afford time-travel machines (though I think that I saw a colleague with a flux capacitor in their office), we are stuck with the linear progression of time. That means that changes in a cause have to happen before the resulting consequences in the effect. Sometimes this time difference is miniscule, so that changes in “A” and “B” seem to happen almost simultaneously, but there is still some ordering. At the very least, if “A” causes “B”, then changes in “B” can not happen before corresponding changes in “A”.

Third, there should be no spurious correlation. A spurious correlation means that some other variable causes both “A” and “B” such that they correlate with each other, and maybe “A” comes before “B”, but in fact there is not causal connection between them. (For a fuller explanation, read this previous post). This is where things get a little tricky. Researchers can measure if two variables are associated, and he or she can measure which came first, but how can you know that there is no secret variable out there that makes the correlation spurious? Who knows, given what “A” and “B” are, there could be dozens if not hundreds possible spurious correlates. How can a researcher rule out all of them? They can’t. The researcher can measure and rule out any obvious spurious correlates, but ultimately it’s an act of faith (or, as it’s called in sociology, “theory”) that a correlation between “A” and “B” is not spurious.

Finally, we like to know how “A” causes “B”. There can be a causal relationship between the two even if we don’t know how they affect each other, but knowing “how” makes us more confident the causal connection. Basically, sociologists sleep better at night if they know the causal mechanism.

So far I’ve discussed this in rather abstract terms, and you’re probably wondering if I had intended to put you to sleep at your computer. (Sociologists sometimes forget that regular human beings don’t get excited talking about vague “A”s and “B”s).

Here’s an example.

Suppose that a friend told you that they had a bag of magic M&Ms. Now, I realize that for some people, any bag of candy is magic, but these are special M&Ms, according to your friend. If you eat a green one, you will instantly become amazingly physically attractive (if you’re not already). You’ll be so handsome or beautiful, that you’ll end up on lists like this, this, and this. (Okay, the last one was just to see if you were paying attention.)

You are intrigued, but you want to find out if it’s true. Does eating green M&Ms make you attractive? Or, to put into boring sociological notation, does “A” cause “B”? To test this, you give a bag of the magic M&Ms to your friends, and then you take notes.

First you notice whether the friends who ate green M&Ms are more attractive than those who didn’t. If so, this would be a positive association—more green M&Ms = more good looks.

Then you would look to see which came first. Perhaps beautiful people just happen to eat more green M&Ms; if so, they “B” comes before “A”, and we don’t think “A” is a cause of “B”.

Can you think of any spurious correlation between green M&Ms and attractiveness? At this particular moment, I can’t (but then again, I may just be thinking about how thin this example is getting).

Finally, you wonder how green M&Ms would change a person so dramatically. You might send them off to the lab and have them analyzed.

Once you’ve answered all these questions, you can decide for yourself if there is hope that green M&Ms will make you so good looking. Then again, maybe you should have some anyway… just in case.

Supporting Traditional Values

By Sally Raskoff

With the introduction of same-sex marriage in California, we are hearing a lot of media reports and informal discussion on this issue. People are “for” it, people are “against” it, people are doing it, and people are picketing it. Polls have been conducted to show us what people in the state and nation think about this issue.

Here is a sampling of the poll results asking people their opinions on the California Supreme Court’s ruling on same-sex marriage:

“Do you approve or disapprove of the recent California State Supreme Court ruling declaring the state’s ban on same-sex marriage as being unconstitutional, thus allowing same-sex couples to marry?”

48% Agree; 46% Disagree (Field Poll, May 7-26 2008, 1052 CA Adult Reg. Voters, 3.2% margin of error)

“The California Supreme Court has struck down the ban on gay marriage in California. Do you agree? Or disagree with the court’s ruling?”

46% Agree; 46% Disagree (Survey USA, May 15 2008, 500 CA Adults, 4.5% margin of error)

“As you may know, last week the California Supreme Court ruled that the California Constitution requires that same-sex couples be given the same right to marry that opposite-sex couples have. Based on what you know, do you approve or disapprove of the Court’s decision to allow same-sex marriage in California?”

41% Agree; 52% Disagree (Los Angeles Times/KTLA , May 20-21 2008, 834 CA Adults, 3% margin of error)

These surveys were done at roughly the same time period and only people in California were contacted. Note the variation in the percent agreeing and disagreeing, the question wording, and the people whom they contacted. The wording of the questions, along with the types of people they contacted can help explain some of the differing percentages. On the other hand, opinions on this phenomenon may vary for many other reasons, such as religious and political affiliations and personal experience. 

To investigate the impact of how we ask about this phenomenon, let’s look at some of the other questions these polls asked.

When people are asked about their preferred form of partnering for same-sex couples, the results are equally varied although less favorable: 

“Which of the following statements comes closest to your view? ‘Same-sex couples should be allowed to legally marry’, or ‘Same-sex couples should be allowed to legally form civil unions, but  not marry’, or ‘Same-sex couples should be not allowed to either marry or form civil unions.’”

35% Marry, 30% Civil Union, 29% Neither (Los Angeles Times/KTLA , May 20-21 2008, 834 CA Adults, 3% margin of error)

“Which of the following most closely resembles your own view about state laws regarding the relationships of two people of the same sex: a) gay and lesbian couples should be allowed to legally marry; b) gay and lesbian couples should be allowed to form civil unions or domestic partnerships, but not legally marry; c) there should be no legal recognition of a gay or lesbian couple’s relationship?”

45% Marry, 32% Civil Union or domestic partnership, 19% No legal recognition (Field Poll , May 7-26 2008, 1052 CA Adult Reg. Voters, 3.2% margin of error)

When asked about legal issues specifically, there is a wider variation in responses:

“Marriages between same-sex couples recognized by law as valid, with the same rights as traditional marriage.”

40% Valid, 56% not valid (Gallup Poll, May 8-11 2008, 1017 U.S. Adults, 5% margin of error) 

“Do you approve or disapprove of California allowing homosexuals to marry members of their own sex and have regular marriage laws apply to them?”

51% Agree, 42% Disagree (Field Poll, May 7-26 2008, 1052 CA Adults Reg. Voters, 3.2% margin of error) 

“Should the decision to marry be strictly a private decision between the people who want to marry or if the government has the right to pass laws to prohibit or allow such marriages between two people who are of the same sex.”

63% Private, 33% Government (USA Today/Gallup Poll, May 30-Jun 1 2008, 1012 U.S. Adults, 3% margin of error)

(Note that the Gallup Poll is of adults in the United States, not just California.)

Some of the studies included questions that asked if the respondent has close family, friends, or co-workers who are gay or lesbian. (One may wonder why they didn’t ask about the respondent’s own sexual orientation.)

“Do you have a friend, family member or co-worker who you know is gay or lesbian, or not?”

69% Yes, 28% No (Los Angeles Times/KTLA, May 20-21 2008, 834 CA Adults, 3% margin of error)

“Do you have any friends or relatives or co-workers who have told you, personally, that they are gay or lesbian”

57% Yes, 42% No (USA Today/Gallup Poll, May 30-Jun 1 2008, 1012 U.S. Adults, 3% margin of error)

When assessing the context of these opinions, one may wonder how these issues resonate with each other. Would having friends or family members or co-workers who are open about their sexuality effect opinions on same-sex marriage? It seems likely, yet few of these polls actually included such a comparison in their findings.

The Pew Research Center for People & the Press issued a report that examined the effect of knowing gay/lesbian people on opinions about same-sex marriage.

They found in their national sample (2,007 adults, Dec 12-Jan 9, 2007) that those who agree that gays should be able to legally marry are more likely to be people who have a close gay friend or family member.  

Beyond the obvious percentage differences, we might as whether these patterns are statistically significant. Taking into account the margin of error (adding to and subtracting from the percentages listed with each poll) we see that perhaps there is less of a difference in opinion and even more variation in these opinions as measured by these surveys. We should use caution when interpreting these results, since any apparent differences could be due to chance, sampling issues, or other problems. Without a statistical test of significance, perhaps we shouldn’t even be talking about these survey patterns as real!

It will be interesting in the coming months and years to see how opinions change – and perhaps to compare these patterns to those of inter-racial marriage (especially from 1950 to the present time) and in other phenomenon we can measure with Social Distance Scales. Created by Emory S. Bogardus, the Social Distance Scale asks respondents how comfortable they are with particular groups, ranging from comfort as members of one’s family to members of society. Do you think people will become more comfortable with gay marriage in the future?

What's a Spurious Correlation?

By Bradley Wright

Every sociology major learns about the concept of spurious correlation, but they don’t always fully understand it. This concept matters because when it occurs, two things look like they cause each other, but in reality they don’t.

Here’s how spurious correlation works. Suppose we have two things that are correlated. This means that when we see levels of one of them change, we usually also see levels of the other change. Because we’re academics, and not always very creative, we’ll call these things “A” and “B” (sounds like a Dr. Seuss book).

If we see “A” correlate with “B”

AB

We might be tempted to assume that “A” causes “B” or that “B” causes “A.”

AB

AB

There may be causation, but there may not be. Before we go much further, we have to look for a third variable, boldly named “C”, that creates a spurious correlation between “A” and “B”. This happens when “C” causes both “A” and “B” and thus produces the observed correlation between them.

We might draw it as follows:

AB

                                       C 

Okay, this is where presentations of spurious correlation usually stop. Some students get it, some don’t. I’m teaching research methods this semester, and I wanted to make this concept more understandable, so I asked the students to come up with examples of spurious correlation based on things that they observed in their everyday life. After reading these examples, you should have a better understanding of how spurious correlation works.

• One student had gone out partying the weekend before, and while sitting in the bar watching his friends during the evening, he noticed that people who had the most fun dancing were also those who were most likely to throw up by the end off the evening. It’s not that dancing made them sick (“A” causes “B”), or that being sick make them have fun dancing (yuck—“B” causes “A”), rather there is a third variable, alcohol consumption (“C”) that leads to both fun dancing and sickness.
• A student works as a nurse at a local hospital. He noticed that the patients who received radiation therapy were also those most likely to die (A and B). Why? Cancer (C) leads to both radiation and death.
• A student has noticed that the mornings when she has the toughest time getting out of bed are also the mornings with the most car accidents on the roads around campus. It’s not that her staying in bed causes car accidents (A-> B), or that she stays in bed out of fear of car accidents (B->A); rather bad weather causes both car accidents and her wanting to stay in bed.
• A student lives in the dorms next to some guys who are varsity athletes. The guys were explaining to her that women are attracted to them (A) because they are athletes (B). She, however, pointed out that it could be because of their muscular bodies (C). Maybe being strong increases their athleticism and it also attracts women.
• A student is from the Caribbean island of St. Thomas. He has noticed that the more people there are on the island, the more crime there is. It’s not that simply having a greater population leads to crime, or that crime attracts more people. Instead, the population swells when lots of tourists arrive, and the tourists commit crimes.
• A student explained that when she burps (A), she also gets the chills (B). She doesn’t think that she burps in response to the chills, or that she gets chilled because she burps. Rather, she just thinks that something weird is happening in her body (C) that causes both.
• Finally, a student and his friends formed a rock band some years ago, and they are trying to make it big. It doesn’t look promising though. As he explains it, the more they practice, the better they sound. Unfortunately, he thinks the relationship between practice time (A) and quality of sound (B) is spurious. The band practices in a friend’s unheated garage and during winter its pretty cold, so they don’t like to practice much. When they do practice, their fingers are so cold, that they have trouble playing their instruments. As such, the cold temperature (C) both decreases practice time (A) and performance quality (B), leading to correlation between them. The student doubts that they are actually getting better, and he may need to plan a career as something other than a rock star.

Where to Sit: Doing Qualitative Research

By Bradley Wright

One of the fun things to do in sociology is to make empirical generalizations. Sometimes in research we start with an idea or a theory, make a hypothesis, and then collect data to test if our idea is correct. This is deductive research, going from large (abstract idea) to small (collecting data about specific people or situations). Deductive research can be very interesting, because we learn if our ideas hold up in the real world, but I don’t think that it’s as fun as inductive research (and as I am aging—about a year annually—I am placing more weight on research being fun).

Sometimes when we enter a situation, even if we don’t know anything about it, we start noticing things. We notice if there are patterns to peoples’ behavior. From these patterns, we create larger explanations about how the social world works. This is inductive because we start with the smaller observation, and from it we build explanations about the larger social world. 

Here’s a simple example of how to create empirical generalizations. In my social research methods class, I asked my students why they sat where they did. It was a reasonable question because the class itself has about 100 chairs, but there are only 50 students, so they had some choice in where they sat.

After talking for about it for about 10 minutes, we came up with the following ways that students decided where to sit.

1) Look for a friend. When you walk into the classroom, first look for someone that you know reasonably well and feel positively toward and sit next to them if there’s an available seat nearby. Or, if you’re really close, see if they’ll move so that you can sit next to them. Don’t sit next to them if you know them well but feel negatively toward them (e.g., an enemy). Also, don’t sit next to them, at least too conspicuously, if you feel positively but don’t know them (e.g., you’re attracted to a stranger).

2) Figure out how close to the front of the room you like to be. If you’re right up front, you catch everything that is going on, but it does make it difficult to sleep, text message, or talk with your friends. If you want to goof around a bit, maybe sit in the back.

3) Find a comfortable seat. Classroom seating is usually pretty tight, with the seats being crammed together—just like economy seating on an airplane. The best seats are those on the aisle. Once class starts, students in the aisle seats can stretch out their legs more than those in the interior seats. The first students to arrive in the class tend to take the aisle seats, and as a result the students arriving later have to step past them to get to the middle seats.

4) Keep an empty seat between you and others (unless you know them). When at all possible, pick a seat that has empty seats on both sides. Seating directly next to someone invades their personal space, and it gives you less room as well.

5) Sit in same area each time. Once you find a suitable seat, try to sit in it, or near it, every class period. This way you get the best seat for you each time, and you don’t really have to think about it. Of course, you may have to change if someone is sitting too close to that seat.

We came up with some other factors that might be incorporated, such as left-handed desks for left-handed students and not sitting directly behind people, especially if they are tall, but the five criteria listed above represented the main decisions made by the students. 

Because students follow these criteria, when I as a professor look out on a classroom, I see alternate seating with only friends sitting next to each other. The aisle seats are always taken. Also, since students tend to sit in the same area each time, I learn to recognize them in 

part by where they sit. In fact, on test days, when I assign random seating, I have trouble recognizing all of my students.

These seating rules are strong enough that they represent social norms, and it can be considered deviant to violate them. For example, if you have friends in a class, but you go sit by yourself, they would probably be upset. Likewise, if there are plenty of empty seats, but you pick one right next to someone, they may take offense.

 

Obviously where to sit in classrooms is a relatively minor issue in the grand scheme of things. Still, it represents a highly structured social interaction, demonstrating the reach of social norms into every aspect of our lives.

New Research on Racial Ethnic Attitudes

By C.N. Le

These days, racial/ethnic relations seem to be at the heart of many of the most controversial issues in modern American society. These issues include the long-running debate about immigration (especially illegal immigration), racist imagery such as the noose recently pictured on the cover of Golf magazine, and issues surrounding Barack Obama's campaign for president.

I think it's useful for us to try to take a step back and look at these specific issues within a broader perspective. Understanding the social context that forms the framework within which each issue unfolds will increase our understanding of them.

With that in mind, let’s look at the results from two recent national-level surveys about the current state of racial/ethnic relations. Studies conducted by the Pew Research Center and New America Media each provide data on attitudes about different racial/ethnic groups in America. 

The Pew Research Center study generally concludes that among whites, blacks, and Latinos, large majorities of each group report that they get along "pretty well" or "very well" with members of the other groups. However, there are some differences -- black and Latino responses seem to be slightly less positive:

While 70% of blacks say blacks and Hispanics get along very or pretty well, just 57% of Hispanics agree. Meantime, some 30% of Hispanics say blacks and Hispanics get along not too or not at all well; this is the most negative assessment registered by any group in the survey about any inter-group relationship.

It's important to note that although the 57% of Latinos who report good relations with blacks is lower than what Blacks report themselves, that 57% is still a numerical majority.

The Pew study also reports that generally speaking, those with higher education and income tend to report better cross-racial relations. Perhaps surprisingly, blacks living in rural areas tend to report better relations with whites than blacks who live in urban or suburban areas. Also, there were no significant differences in terms of attitudes by region of country. Finally (and most discouraging), younger blacks report worse relations with whites than older blacks do.

In general, I found the Pew study informative but with one significant drawback -- they chose to exclude Asian Americans from the study.

In my opinion, this omission is inexcusable at a time when the Asian American population is close to 15 million, in which Asian Americans are some of the most socioeconomically successful ethnic groups in the U.S., and when Asian Americans increasingly make up large proportions of the population of many states and majorities in many cities.

To remedy that, let's turn to the other national study on racial attitudes, from New America Media (NAM), in conjunction with Bendixen & Associates. This survey included Asian Americans, Latinos, and African Americans, but because it focused on attitudes among and between racial/ethnic minority groups, the study did not include whites.

I am impressed that the NAM study was conducted in multiple languages to maximize its overall validity and accuracy. A PowerPoint presentation of their major findings is also available for download. To summarize, the study notes:

[The poll] uncovered serious tensions among these ethnic groups, including mistrust and significant stereotyping, but a majority of each group also said they should put aside differences and work together to better their communities.

Predominantly immigrant populations - Hispanics and Asians - expressed far greater optimism about their lives in America, concluding that hard work is rewarded in this society. By contrast, more than 60% of the African Americans polled do not believe the American Dream works for them.

[Regarding tensions and mistrust], 44% of Hispanics and 47% of Asians are “generally afraid of African Americans because they are responsible for most of the crime.” Meanwhile, 46% of Hispanics and 52% of African Americans believe “most Asian business owners do not treat them with respect.” And half of African Americans feel threatened by Latin American immigrants because “they are taking jobs, housing and political power away from the black community.” 

[Nonetheless], the poll found “a shared appreciation” for each group’s cultural and political contributions. “Hispanics and Asians recognize that African Americans led the fight for civil rights and against discrimination, forging a better future for the other groups.”

I am saddened to hear that apparently, there is still a lot of racial tension and suspicion between Asian Americans, Latinos, and African Americans. I agree that important issues need to be addressed for these stereotypes to eventually be debunked.

Nonetheless, two points from the NAM survey stand out. The first is that as the Pew Research Center study generally showed, more educated and higher- income respondents are likely to be more positive about cross-racial attitudes and experiences.

With that in mind, it appears that the NAM survey did not disaggregate its responses by social class, and instead lumped everyone from all kinds of educational, income, and occupational backgrounds together within each racial/ethnic group. This categorization unfortunately distorts the findings a little bit.

But I am more disappointed in some of the wording of the questions in the NAM survey. For example, it asked Asian and Latino respondents whether they agreed with the statement "I am generally afraid of African Americans because they are responsible for most of the crime."

The wording of this question is biased, leading, and confusing. First of all, it asks two questions in one -- whether they are afraid of African Americans, and two, whether they agree that African Americans commit most of the crime. One of the key rules about questionnaire design is that you should only ask one question at a time.

Second, presenting the statement that African Americans "are responsible for most of the crime" is leading -- it should have just asked the question, "Do you agree or disagree that African Americans are responsible for most crimes committed" would have been less leading and more direct. The distinction between the two is subtle, but empirically important.

Another example of a poorly-worded and misleading question posed to African American and Asian respondents is the one that begins: "Latin American immigrants are taking away jobs, housing and political power from the Black community." Again, the problem here is that there are three questions combined into one -- whether Latino immigrants take away jobs, take away housing, and take away political power are all three distinct issues and questions that are unfortunately all rolled into one.

Taken together, these two questions may have distorted and exaggerated the overall level of racial tension between Asians, African Americans, and Latinos, especially considering most of the other findings in the NAM study, which generally showed a high level of willingness to cooperate with each other.

Specifically, 86% of Asians, 89% of African Americans, and 92% of Latinos agreed with the statement, "African Americans, Latinos, and Asians have many similar problems. They should put aside their differences and work together on issues that affect their communities."

Ultimately, that is the probably the most significant finding from the flawed NAM survey. Although some tensions and stereotypes still exist between Asians, Latinos, and Africans Americans overwhelming majorities of each group are willing to work together to address issues of discrimination and inequality that they have in common.

Both the Pew and NAM studies offer useful and interesting data, but the shortcomings in their fundamental design compromises their overall value.

Matching Research Methods to Research Questions

By Janis Prince Inniss

Dr. James Watson, the 79- year-old American scientist credited with co-discovering the DNA double helix recently told a reporter that black people are naturally less intelligent than whites, and that although he wished this was not the case, “people who have to deal with black employees find this is not true.”

At the very least, Watson seems to have a penchant for making outrageous statements, but I refer to him not to discuss the obvious. Instead, I want to focus on the data on which he based his headline-grabbing conclusion. Or put another way, how could we attempt to compare intelligence levels of blacks and whites? What would be some reasonable sources of data from which one could conclude that black people are “naturally” less intelligent than whites? 

First, we would have to define the two groups of people: whites and blacks. What criteria would we use to define blacks? And whites? Would we apply the “one drop rule” as I discussed in a previous post on Tiger Woods? Would we use people’s self-definitions? 

Would we, as researchers, assign people to one of the two groups based on appearance? What if someone ”looked” white or self- identified as white, but has a black parent or grandparent? And given that the comparison is of blacks and whites, with no mention of country, we would have to include people from around the world in our study; this can be quite complicated, as conceptions of race vary significantly in different parts of the world. And assuming we could come up with acceptable definitions in any one country, we would have to use these definitions all across the world in order to have common definitions for the study -- regardless of how alien they are to others. 

Let’s say that we managed to come up with a fairly precise definition of blacks and whites that would work in the real world, all over the world. What would be the next step? What research method would we use to answer this empirical question?

If we chose to conduct an ethnography, (and do a good job) we could produce rich data, but of what nature? Ethnography includes interviews and participant observation; we could interview people and get their opinions and thoughts about the intelligence levels of blacks and whites. Fascinating as this might be, it would not answer our research question. We could observe blacks and whites and opine about their intelligence, but any conclusions would not address the central question posed by our research. Further, our observations will likely be biased by any preconceived notions we may have, and we might notice examples that justify our beliefs more than those that do not.

Besides ethnography, we could develop a questionnaire to measure intelligence or use an existing IQ (intelligence quotient) test and mail them out to people and/or administer them ourselves. Clearly, administering or mailing the questionnaire to every black and white person in the world, or even in the U. S. is an impractical task.

Therefore, we would have to focus on a sample or a fraction of the world’s population of blacks and whites. Once the sample is carefully chosen, we could feel confident that it represents and applies to the entire population. In our case, every black and white person in the world should have an equal possibility of being involved in our study; then, we would randomly choose our sample. 

As an illustration, in order to conduct a study with a random sample of college students at your university, we would obtain a list of all students and then, for example, include every 10th name in the sample. Once we had figured out how to obtain a random sample of the world’s population of blacks and whites, we would administer our IQ test to the sample. Managing the cost, resources, and other logistics for a survey of this magnitude is not a responsibility I would sign up for, but I suppose it could be done. 

I have devoted more time discussing the definition of black and white, which might suggest that defining intelligence is easy; it is not. What is intelligence? What kind of intelligence would we measure? Emotional? Spatial? Conceptual? Mathematical? All of the above and more? 

If you are familiar with some of the criticisms of IQ tests, you would have raised an eyebrow at their earliest mention. How would we account for differences in access to formal education for example, given that less formal education results in lower IQ test scores? How would we sort out social and cultural factors that related to scores? 

It is shocking to me that a scientist, a Nobel Laureate, would mention the experiences of unspecified “people” as a source of data regarding anything, including his claim that black people are less intelligent than whites. The claim implies a biological difference between the races and fundamentally would require an ability to differentiate between blacks and whites in some absolute manner; this is difficult to do when race is a socially constructed concept with no biological basis. 

I wonder which “people” was Watson referring to? How does he know what “people” believe or think? Did he interview some people about this question? How many such people did he interview? Good, in-depth interviews provide a deep understanding of behavior or beliefs, but no evidence regarding a biologically based difference between races. Did he send questionnaires to a random sample of “people”? Surveys could provide the opinions of a large number of people, but not some “truth” about blacks and whites. What rigorous research method was Watson employing?

Even a fledgling scientist recognizes that research is a process, and that matching the research method to the research question is critical. Sadly, when such a highly regarded scientist makes outrageous claims, those with little understanding of the scientific method (and more than a little prejudice) might just believe that he is right.

Trend Spotting: Suicide

By Karen Sternheimer 

When you see stories about trends in the news, do you ever stop and think about what it really means? I suspect most people take stories with statistics at face value; after all, if there are numbers to back up the story’s claims, why think twice? 

In this occasional feature, I will look deeper at a couple big, attention-grabbing headlines to question whether the so-called trend is really newsworthy or just another attempt to make a story out of a footnote. 

Is suicide a growing problem? 

According to the Los Angeles Times, yes. An article with the scary headline “Jump in Youth Suicides Reverses Trend” ran in early September, following the release of the Centers for Disease Control and Prevention's (CDC) Morbidity and Mortality Weekly Report. The article notes a scary-sounding jump of 76% in suicides of girls ages ten to fourteen, and states that the overall suicide rate for people 10 to 24 increased “substantially,” by eight percent. 

Okay, now let’s look into these numbers more closely.   

1. Ten- to fourteen-year-old girls are highly unlikely to commit suicide. 

As the Times article concedes, the 76% rise represents the difference between 56 suicides in 2003 to 94 in 2004….out of a population of nearly ten million girls. Yes, 76% seems like a big number, but when we put it into perspective its actual meaning isn’t as dramatic as the percentage makes it sound. While each represents a tragedy, 38 more girls committing suicide still means that less than one in a million American girls in this age group committed suicide. 

 

2. Boys are far more likely to commit suicide than girls are. 

If you take a look at the graph above (I recommend clicking on it to see it best), you can see that at every age, boys and young men are more likely to commit suicide than girls are. Historically, this is because boys have been more likely to use more violent (and thus more lethal) methods to harm themselves than girls are. 

3. In spite of a slight up tick, suicide rates among young people are way down from a decade ago. 

Again, if you look at the graph above, the big story is that trends for females are basically flat in the long term and way down for teen boys and young men. Once again, any loss of life shouldn’t be minimized, but as far as major trends are concerned, the news is basically good. Statisticians call small movements up or down “noise” in the data; in other words, it is statistically unlikely to find the same number of suicides year-to-year. Take a look at the end of the graph—do those differences that really seem “significant” in context?   

4. The older a person is, the greater the likelihood they will commit suicide. 

Here’s the real story, the one that’s usually hidden. It’s not your teenage brother you should worry most about, but your grandfather. With the exception of the 65- to 74 year-old age group, suicide rates steadily increase with age. You might be surprised to find out that according to CDC data, the suicide rate for men aged 85 and older is 47.8 per 100,000 (in contrast to the headline-grabbing .95 per 100,000 for 10-14 year-old girls). Elderly men are more than twice as likely to die of suicide than their 18-34 year-old grandsons, which is rather remarkable considering young men are far less likely to die, period (although they are more likely to be the victims of homicide than their parents or grandparents are). 

Why do you think we pay so much attention to teen suicide and all but ignore the rates at which adults take their own lives? I suspect it has something to do with the widespread assumption that teens are inherently unstable. But obviously life can bring significant challenges at any age. 

It is no less of a tragedy when someone in their middle or late adulthood decides to end their life. I myself have known three people who killed themselves (two were only about thirty, and one person was elderly) and have seen the horror, pain, and ongoing sadness their loss causes their loved ones. Trust me, it’s no easier to deal with a suicide if the person is older and well out of their teens. 

Ironically, the suicide story we hear over and over teaches us very little about suicide, but it does tell us something about our perceptions of young people. This “teen suicide” theme fosters the dubious belief that young people need ever more monitoring and control. What the actual trends tell us is that we need to invest far more resources in providing quality, comprehensive mental health care to people of all ages. 

Are America's Schools Safe?

By Karen Sternheimer 

The elementary school on my street is once again brimming with excitement and back-to-school jitters. (It’s always hard to tell who is more nervous, the children or the parents.)   

As the new school year began and parents packed their kids off for classrooms and dorm rooms, this school year might bring some extra worry, with the Virginia Tech shooting last April reopening old Columbine High School-style wounds. And last year’s shocking shooting in a rural Pennsylvania Amish school made it seem like no school was really safe. 

But the truth is schools are among the safest places for young people to be. 

Still, fears of a rampage-style shooting linger as the school year begins again. School-based law enforcement, which is lobbying for a piece of Homeland Security funding, is among the fastest growing sectors of the security industry. 

In our quest to ensure that kids are safe, we’ve overlooked one key fact: crime in America’s schools is on the decline. 

Overall, violent crime has fallen sharply since the early 1990s. Homicide arrest rates among juveniles in particular plunged by 77 percent between 1993 and 2003. School-aged kids are 122 times more likely to die in an accident than die at school. Five- to 14-year-olds are four times more likely to die of pneumonia or the flu than to die at school. 

According to the U.S. Department of Education, crime in schools was cut in half between 1992 and 2002 and has continued to decline since. Serious violent crime remains rare in school – the vast majority of schools report none. The most common form of violence is one many of us likely remember well: the old-fashioned fistfight. 

Even during the 1990s, when fears of school shootings ran high and violence was at its peak, students had less than a seven in 10 million chance of being killed at school. College campuses are also very safe. This year’s horrific incident at Virginia Tech was clearly an aberration—campus violence is considerably lower than it is off-campus across the nation. 

The few schools that do have considerable safety problems still tend to have far lower crime rates than their surrounding neighborhoods do. Urban high school students are three times more likely to be victimized away from school than on school property. And in suburbia, students are still twice as likely to be victims of violence away from school grounds than while at school. 

Regardless of where they live, kids are significantly safer at school than anywhere else. Children are much more likely to be victimized by adults than by each other. Statistically, kids are actually safer in the company of other students than they are with their parents. And for young people, being engaged in education may itself act as a protective factor against violent victimization and criminal involvement. 

While killings within families and at workplaces vastly outnumber school shootings overall, when violence does happen at schools it strikes a particular chord. As sites connected with both learning and youth, schools represent repositories of hope for the future. 

Children’s safety in schools should remain a primary concern. We may all feel better knowing that security equipment and emergency procedures are in place. But some districts have arguably overreacted and put policies in place that may satisfy anxious parents but do little to improve school safety. 

For example, so-called "zero tolerance" policies employed in schools across the country mandate increased punishments for the most minor infractions. Sounds good on paper, but the reality is that many kids who have been suspended based on these rules had “weapons” such as manicure kits and fingers pointed like guns, or had thrown potato chips at another student. Understanding intent goes out the window when we become so afraid that a student with a steak knife used to cut an onion for a science project demonstration gets suspended. A 2001 study, published in the journal Educational Leadership, found that eight in ten students disciplined under zero tolerance rules were not serious threats to school safety. 

Recent events can re-open old worries about school violence and mask the reality that schools are significantly safer now than they were a decade ago. Safety is an emotional issue, one that parents and politicians can agree is important. 

There is a danger, however, in focusing so much on unlikely events that we ignore many of the complex issues plaguing so many schools: overcrowding, outdated materials, decaying facilities and overwhelmed teachers, not to mention alienating students with rigid one-size-fits-all policies. This, coupled with skyrocketing tuition at colleges and universities means that many are being shut out of higher education entirely, giving them less reason to commit themselves to education. Perhaps the biggest danger facing our nation’s schools is using our scarce resources to massage our fears rather than to educate a generation.

Does Finger Size Reveal Sexual Orientation?

By Sally Raskoff

Do you think you can tell a person’s sexual orientation just by looking at them? A recent New York Magazine article by David France suggests that there are physical characteristics that indicate one’s sexual orientation as straight, gay or lesbian. Lately, research has focused on such markers as a person’s finger length, fingerprint density, the direction that hair swirls on one’s head, and left or right handedness. 

According the researchers France cites, finger length, or, more specifically, the ratio of the second digit (index or pointer finger) to the fourth digit (ring finger), can indicate sexual orientation for both men and women, although in opposite patterns. Men with a smaller ratio (index fingers are shorter than their ring fingers) would indicate a heterosexual orientation while men with a larger ratio (index fingers longer than their ring fingers) would indicate a homosexual orientation. Alternatively, women with a larger ratio, those deemed heterosexual, would have longer index fingers and women with a smaller ratio (index finger shorter than their ring finger) would be lesbian. 

Did you stop reading here to look at your fingers or your hair? I did when I first heard the story on NPR’s Talk of the Nation.

The article has received a lot of media attention (including from Comedy Central's The Colbert Report) which seemed to assume that these patterns are real and reliable. Thus the public who hears about this research might assume that one can identify people’s sexual orientation by their relative finger lengths and hair swirls. 

As a sociologist, this news story has me wondering what the researchers actually said and, if indeed these are valid and reliable findings, how strong are these supposed patterns? 

Looking up related peer-reviewed research on my campus library’s webpage, I found many articles relating to these issues. Many of them do find statistically significant differences in these characteristics between those who identify as heterosexual, gay, or lesbian; but it took me a while to find the details on the strength of these patterns. But depending on the characteristic in question, the significance of these patterns is not overwhelming. 

Let’s start with handedness. A study published in 2003 by Richard Lippa found that homosexual men have an 82% greater chance than heterosexual men of being non-right handed (left handed or ambidextrous). This is a statistically significant difference based on his research finding that 11.4% of heterosexual men and 19.0% of homosexual men are non-right-handed. His findings for  women were not significant. 

Regarding the finger length issue, studies have found differences depending on gender, ethnicity, and which hand one is looking at. A comprehensive analysis of this research published in 2005 highlights that in the five studies they analyzed, there is more variation in the finger length ratio among heterosexual people than there is among homosexual people.

Many of these studies are done to assess whether genetics or in-utero conditions have a greater effect on human development. Another study of finger length used a sample of identical twins (who of course have identical genes) to assess the role of genetics versus the “prenatal environment.” The researchers tested seven twin pairs who were “discordant” for sexual orientation (one twin was heterosexual and the other homosexual in orientation), and five twin pairs who have “concordant” (the same) sexual orientation. Their findings did indicate a statistically significant difference in ratios for both the left and right hands, with the heterosexual twins having greater ratios between their second and fourth fingers than their homosexual twins. 

If you look closely at the twin data, however, you’ll see that the differences in ratios are not overwhelming. In the analysis of data from five previous studies, the ratios for each gender group were also not overwhelmingly different across sexual orientation groups.

All in all, my journey into the actual research did show that there are some interesting physical differences across groups. However, those patterns can easily be misinterpreted and applied in inappropriate ways. Just because the researchers found statistically significant differences does not mean that having a particular finger ratio indicates homosexuality. For example, in the handedness study, while gay men may have a higher chance of being non-right-handed compared to straight men, it does not follow that left-handed men are gay-- keep in mind that less than 20% of men in either group are non-right handed! 

Studies on finger length ratios and the other characteristics alleged to be associated with sexual orientation are vulnerable to the same kind of interpretation error. If you look at your index and ring fingers and assess which is longer, does this indicate your sexual orientation? Some people might question their sexual orientation once they hear of this research and identify their own finger ratio. However, this is a misuse of this research and a misinterpretation of the data. While many straight men and lesbian women may have shorter index fingers relative to ring fingers, there will be plenty of straight men and lesbian women who have comparatively longer index fingers. Likewise, there are many gay men and straight women with same length or longer index fingers, there are also gay men and straight women with shorter index fingers.

When I see news items that mention relationships like these, I know that it’s a good idea to see what the researchers actually said and if the public hears the same story. The media’s job of translating the scientific world for the public is a difficult task and is sometimes impossible considering the lack of scientific expertise of most reporters, restrictions on time, the need to avoid scientific language, and the wish to have the simplest explanation of very complex phenomena. Since the media is often more concerned with generating the most sensational headline than it is with relaying information accurately, it is imperative that we educate ourselves to assess research studies ourselves and to identify the most accurate information in them.

Having established that there are statistically significant patterns in finger length, does this mean that homosexuality creates different finger lengths? Or that finger length ratios create homosexuality? Remember, these studies cannot support a strong causal relationship between these characteristics and homosexual orientation because of the high levels of variation within groups. This brings to mind an important phrase you may have heard in sociology or other classes: correlation is not causation.

Murder and Statistics

By Karen Sternheimer

If you saw the national news on June 4^th, you probably heard a frightening story. The FBI released their preliminary report of crimes that occurred in 2006. According to the report, murder is on the rise—up 6.7 percent in America's  biggest cities. Experts warned we could be in for a new crime wave, and offered explanations for the upswing. Should we be afraid?

Maybe. Or Maybe not.

Let’s consider the basic implication when we hear grim statistics like this: things really are getting worse, and there is a number to prove it. But in this case, and in many others, we only hear some of the numbers; maybe just one if it seems to tell a dramatic story. What else should we consider before deciding that America really is a more dangerous place?

First, fortunately, murder is one of the rarest crimes. Of all violent crimes reported to police in 2005 (which includes crimes like rape, assault, and robbery), only 1.2 percent of those were homicides. But we have a fascination with murder—think of what would become of network television dramas if shows like CSI or Law and Order weren’t on the air? We are attracted to popular culture that helps us to deal with the scariest parts of the human experience from a safe distance. But sometimes this focus can make us think that the world is a more dangerous place than it actually is.

Second, let’s get back to the 6.7 percent rise that the major news networks grimly reported. Based on ten cities with populations over one million, this number reflects approximately 194 more homicides than in 2005. No doubt, every homicide is a tragedy and has a ripple effect that goes way beyond the victims themselves, but this is a relatively small number when you consider the combined populations of those ten cities.

Just over 25 million people live in the nation’s largest cities, and according to these early reports in 2006 3,085 people were killed, or one-tenth of one percent of the population.

By contrast, nearly 95 million Americans live in smaller cities that experienced reductions in homicide. Cities with 50,000 to 99,000 residents actually experienced a 6.9 percent decline in 2006, but this rarely made it into the news reports.

Why the omission? Could it be that fewer people would be affected in smaller cities, and the national news media wouldn’t report on something that only impacts a small group? Actually, more people live in mid-sized cities than those with more than one million residents.

Bad news gets our attention. Consider this headline: “Murder Rises Three-Tenths of a Percent!” Not impressed? Apparently news organizations weren’t either, but that’s the overall change from 2005 to 2006 nationwide. This change translates to about fifty more homicides in a population of 300 million. Again, each murder is a tragedy, but three-tenths of a percent raise hardly suggests a major upward trend.

Hearing about rises in crime from year to year, no matter how remote, is still frightening--sort of like a reverse-lottery you don’t want to win. News reports have a way to stoke this fear. They are not