firstcontactI am always on the lookout for some new teaching ideas (I teach a 5/5 load, people, you have to find new things to do if you are not going to insane). So, I got First Contact – Teaching and Learning in Introductory Sociology, hoping it would contain a lot of ideas about teach intro (something I teach A LOT). The book was also reviewed in the July 2014 of Teaching Sociology (which is where I saw it mentioned). So, I decided to read the book before reading the review.

I have to say that this book turned out to be a major disappointment. The only way anyone can find this book useful is if they are completely new at teaching, as in, no teaching experience whatsoever, or completely clueless about this whole teaching business. So, if you are in that position, starting to teach from scratch, and this is your first introduction class ever, then, you might find this book helpful.

So, it may very well be that I have been teaching for a long time (I taught my first class in Spring 1997, language in society, as a graduate student, to linguistics major, at the University of Nice, in France). But I think that no matter how long one has been teaching, there is always room for improvement. And frankly, teaching has changed dramatically in the past 17 years of my teaching career. Technology has dramatically altered how we do things. Online education (or “education”, if one wants to be cranky about it) and hybrids have exploded into the field of digital learning. So, this isn’t your grandfather’s introduction to sociology anymore.

The interesting thing is that the basic building blocks of introductory sociology courses has not changed from where I started to teach in the United States in 2000. You just need to look at the table of content for any sociology text and go back 15 years, you won’t find much change in the way we teach introduction to sociology. So, any changes or innovation have to come from somewhere else. I was hoping the book would address the “somewhere else”.

I was also hoping to get some ideas about the perennial struggle of the sociology instructor: fight the psychology bias of American students, along with commonsense, and half-baked economic ideas.

While the book acknowledges all of these challenges (changes in teaching with increased focus on learning, the persistence of how we teach introduction to sociology, and the individualistic bias of our audience), it never really addresses them. And that is the main problem with this book: it remain much too general to be of use. The book painstakingly goes over every minute components of the syllabus but this is the wrong focus and that is not useful because this is information that is either largely provided by one’s institution, and it is not hard to find a generic template. One does not need a book for that.

The second major issue, to me, was that the book is not enough about sociology. A lot of what is mentioned, whether it’s about assessment or student engagement, could apply to any other discipline. Most of the time, the book reads like a compendium on best practices in teaching rather than specifically about teaching introduction to sociology.

The specific challenges of teaching sociology get only superficial treatment. When it comes to selecting course materials or discussing sociology directly, or reviewing the literature on teaching sociology, some of the references used date from the 80s or 90s. Sorry, but that does not cut it and it does not help dealing with contemporary issues in teaching introduction to sociology. Part of the frustration was that the book never really takes a stance on anything, whether it is on textbook and material options, or anything else. It lays out the issues but never really deals with them or takes a position.

So, again, if you are brand new to teaching, then, maybe, you’ll find this book useful and helpful. But if you have the slightest bit of experience, then, frankly, it will be waste of your time. Which is a shame because there is a need for a book on this topic, but this one is not it.

We all love to teach statistics to undergraduate students, don’t we? Of course we do. </snark>

We know teaching stats is not easy and students hate maths (and some of us do too). And yet, it is a rite of passage and we all have to get through it. Well, The Cartoon Introduction to Statistics by Grady Klein and Alan Dabney might help. As the title helpfully notes, this is a cartoon book. It is also a very basic introduction to statistical concepts and ideas. May I emphasize: very basic.

And let me emphasize something else: no maths.

That’s right. There is a little appendix at the end with a few formulas but nothing much really. The whole idea is to focus on concepts, not technicalities and maths. In other words, this book is not a substitute for a regular statistical textbook. But it might make digesting the maths a bit easier. This book might be a nice addition to existing course materials if you are looking for something a little lighthearted and humorous.

I should also add that this book does not cover the entirety of the usual curriculum and topics that you would find in a regular undergraduate statistical course.

The book tries to convey a sense of how pervasive and useful statistics are in daily life. It uses concrete examples, again, with some humor. It does cover descriptive statistics, measures of central tendencies, normal distribution, the central limit theorem, a little bit of probabilities (but really, not a whole lot), inference and hypothesis testing, confidence levels and intervals. Again, with no maths.

There is a single idea that drives the entire book (and one that makes it, at times, a bit repetitive): one can really never know about the characteristics of an entire population, but we can know some things about parts of that population, through statistics. That is the main thesis. However, we can never be 100% sure of the information we get through statistics, because statistics do not measure entire populations, just little chunks of it, that is, samples. This is the theme of the book and this gets repeated in almost every chapter.

I would think that undergraduate students would find such a book attractive and fun to get through. The fictional examples used are indeed pretty fun (dragons, vikings, monsters, aliens, and Crazy Billy’s Bait Barn). Again, this will not substitute for textbooks, real maths, and real statistics professors, but this might make a nice (and relatively cheap) addition to any course.

Now, the cartooning… After all, this is a cartoon introduction. If you follow this blog, you know that we have a gallery of sociologists cartooned by Kevin Moore. I was not thrilled about the cartooning in the book. It might be partly because Kevin Moore has completely spoiled me because his cartooning is so great. The cartooning in Klein and Dabney’s book was, I think, a bit “fuzzy”. I tend to think clear-cut things and the cartooning felt unfinished and a bit sloppy. It was not the grey scale. I was ok with that and full colors might have actually made the whole thing look too busy. I just wish the drawing had been clearer and neater.

But, again, this might be worth recommending to students who are a bit worried about having to take a statistics class. More than that, I think there is a lot of room for more cartooning introduction to sociology-related topics.

Haven’t you heard that answer when you asked students questions such as “why is the homicide rate higher in the US than in other high-income countries?” or other some such questions? And you push forth explaining rates and ratios and all these things, trying to be as convincing as possible… until the next question comes up and you get the same answer: it’s because there are more people. It’s as if there is some automatic belief that a larger population will automatically cause more of something (whatever it is).

And here comes along Danny Dorling with the same puzzle and a brutal but good answer, from his latest book, Population 10 Billion:

“It was at this symposium that the Ugandan Minister of Finance and Planning, the Honourable Professor Ephraim Kamuntu, felt he needed to point out to the audience that ‘. . . the developing world contributes the least greenhouse gas emissions, that they will be most affected by climate change, and that they are least able to deal with the negative effects’. He was then, in effect, rebuked by the keynote speaker, Jonathan Porritt, son of the former colonial governor of New Zealand, who ‘. . . reminded the audience that we need to get beyond the “crass” consumption versus population debate’. 27 But Kamuntu was right and Porritt was wrong. What is crass about explaining that it is consumption, not population, that matters, and why does Porritt either not appear to understand that, or not want us to understand it? Does he want a world with fewer people but where a minority can still consume very highly, in place of the thousands who don’t exist?

Suggesting that consumption and population both matter is identical to suggesting that when it comes to murder, both violence and population matter. The higher a level of violence you have, the more murders you get, and simultaneously, the more people you have, the more murders you get, as there are more people available to murder. This is simply stupid. Murder rates fall in countries where levels of violence fall, even as population rises. Our rate of murder, if the number of holes in ancient human skulls is any indication, was highest in our distant past. Most of us have never been as peaceful as we are today.

(…)

Proponents of population scare stories say that as every extra human must consume something, this argument does not apply to consumption. You cannot have a negative consumption rate for a person. However, the same is true of murder. You cannot have a negative murder rate for a group, but some extra people can help others to murder less, just as some extra people can teach others to consume less and hence reduce consumption overall, even as population rises.” (Loc. 1767-1789)

In the soon-to-no-longer-be-used Microcase workbook I have been using for years, one of the exercises, in the chapter on socialization, involved looking at what particular traits people think children should possess, in different countries. The data for this exercise came from the World Value Survey. In the WVS, you can only download full datasets, formatted for SPSS, SAS, or STATA, which I don’t have at home. However, the website offers a neat analysis tool where you can conduct some analysis in your browser, selecting the variables and countries you want, and then, download the result in Excel. Thanks to that and Tableau, I was able to reconstruct the exercise.

Results below: bar charts showing what percentage of surveyed people, in selected countries (missing a lot of data from Africa, as usual), think children should have the following traits:

Determination / perseverance

In this case, I think it is more interesting to look at which countries do not really value that trait all that much, that is, the bottom of the list, rather than the top. And what’s with Switzerland?

Responsibility

Note how the percentages go way up compared to the previous one, where the maximum value was 72.5%. Note also the strong showing of Asian countries toward the top.

Hard work

Note the absence of Western countries from the top and their stronger presence at the bottom. I blame Montessori education and pop psychology, and also, affluence.

Imagination

And here, Western, wealthy, countries make a strong showing at the top, not very surprisingly. But note how low the percentages are, even for the top, and how really low they are at the bottom.

Independence

This one leads to more mixed results and not particular geographical trends.

Obedience

Here again, it is not surprising to find no Western countries at the top, but poorer, and, one can assume, more traditionalist countries where obedience is more valued. It is interesting to find Japan and Hong Kong way at the bottom, with very low rates.

Tolerance and respect

Here again, we find Western countries at the top, considering tolerance and respect are fairly liberal values.

Religious faith

Countries with strong Muslim populations take the first three slots. After that, the rates go down pretty quickly. Why is Hong Kong always at the bottom?

Thrift

Asian countries occupy the top on that one (except Hong King, again, at the bottom). Wealthier countries, overall, don’t seem to care all that much.

Unselfishness

The top percentages for this one are not all that high to start with. Very quickly, the percentages get under 50%. Why would that be?

[Note: for all the visualizations, you can get larger views by clicking on the images.]

I have never found culture to be a particularly difficult or painful topic to teach in introduction to sociology classes. There is though a tendency in my students to perceive the relationships between technology and culture to be a one way street: technology -> culture and behavior. One of the challenges is to explore the other way around in that relationship: culture -> technology -> behavior or culture -> behavior -> technology. And that is even before you start discussing the social structures of technological production and the social institutions involved in the process (education, government, military, etc.). And that is before you even begin to discuss the fact that technology access and use is never homogeneously distributed within and between population (e.g. the digital divide), and that layers of social stratification are crucial in that respect.

But anyhoo, it is always interesting to trying to explore some data on technology as part of a larger discussion on other non-material aspects of culture, as they are all interrelated. This is my attempt at doing so (I’ll deal with values and norms in another post). I got all the data from Gapminder. The maps and bar charts were done in Tableau (sorry I still cannot embed my workbooks here, so, links to Tableau Public will have to do). The Gapminder screengrabs are what I did when I combined technology variables with GDP per capita (as a rough measure of economic development). Click on the screen grabs to go to the Gapminder interactive graphs. At the same time, because the technologies I an discussing below are relatively recent, the time series are not as crucial as they might be for other variables.

Anyhoo, feel free to use these visualizations as part of exercises to get students to think about data, patterns and technology.

So, one easy way to start is with personal computers.

Personal computer per 100 people (interactive world map and rankings via Tableau)

Static map:

PC per 100 map

It is not hard to see the big gap between North America / Western Europe and pretty much everybody else. This is more visible in the rankings.

Static partial rankings:

PC per 100 - Partial rankings

Personal computer per 100 people correlated with GDP per capital (bubble chart where bubble size reflects population size, via Gapminder)

Static image:

PC per person and GDP

I would think that 2006 data for PCs are already pretty old data, but that was the most recent on Gapminder, so there you have it. As you can see though, coloring the bubbles based on GDP per capita helps identify a pattern of higher income -> higher availability of technology, but with a very steep curve (therefore, a bigger gap between high PC users / high GDP per capita versus the rest). One interesting question is whether this clear pattern will persist across other related technologies.

Like cell phones:

Cell phone rates per 100 people (interactive world map and rankings via Tableau)

Static map:

Mobiles per 100 people - Map

The rates are markedly different than those for PCs (and the data a bit more recent). Anyone who travels to the Global South has to notice the omnipresence of mobile phones. It looks like a lot of countries of the Global South practically bypassed the landline stage and went straight to mobile. The rates reflect that, even though stratification patterns are still obvious.

Partial rankings – static image:

Mobiles per 100 people - Partial rankings

Cell phone rates per 100 people correlated with GDP per capita (bubble chart where bubble size reflects population size, via Gapminder)

Static image:

Cell phones oer 100 and GDP

As you can see, this is a very different bubble chart than the preceding one. No steep, highly stratified curve here. There is definitely a positive correlation between GDP per capita and mobile phones subscriptions, as reflected by the color gradient, but the curve is more regular and linear.

Internet use rates per 100 people (interactive world map and rankings via Tableau)

Static map:

Internet per 100 map

Regional patterns are rather clear on this one. Of course, the data only measure internet use, but not the quality of connections and networks.

Static partial rankings:

Internet per 100 - Partial rankings

One can clearly in which countries full access is available and achieved.

Internet user rates per 100 people correlated with GDP per capita (bubble chart where bubble size reflects population size, via Gapminder)

Static image:

Internet per 100 and GDP

Here, we get a third different pattern: steep and linear, with the same stratification.

So, we know that Internet use is pretty widespread, except for the poorest countries in Sub-Saharan Africa, but as I mentioned above, Internet use does not measure quality of connection. So, let’s look at broadband.

Broadband rates per 100 people (interactive world map and rankings via Tableau)

Static image for map:

Broadband per 100 people - Map

Static partial rankings:

Broadband per 100 - partial rankings

The rates are overall much lower, across the board (with two top outliers), so one can see that broadband penetration still has a way to go, even in the richer countries.

Broadband rates per 100 correlated with GDP per capita (bubble chart where bubble size reflects population size, via Gapminder)

Static image:

Broadband per 100 and GDP

And here is a fourth different pattern: a lot of countries at the bottom of the graph, close to 0, with a very flat pattern, a slow rise as we get towards the high GDP per capita countries, a very smooth and slow upward curve (with our two outliers in the top right corner).

So, again, even though the different technologies I have visualized here are related to each other, one can see that their access varies widely, by technology, by regions, and by levels of development. At the same time, comparative graphs also show different patterns of global penetration of these technology, from brutally stratified curves (PC), to almost perfectly linear (mobile subscriptions), to steep curve (Internet), to long left-tailed curve (broadband). The trick would be to try to find explanations for these different patterns.

And, of course, we focused only on the technologies themselves here. Not on what people do with them and the different forms of usage. That is for another post.

Ok, so yesterday, I went over a possible exercise I had in mind for a module on data analysis for my introduction class. Today, I thought I might as well share the databases that I have started collecting for the purpose of designing more such exercises on different topics. So, here is a partial list that I will constantly update as I find good stuff on the Intertoobz.

International Organizations

Other Global Data

US Data

There are others, I’m sure. And I’ll add them as I find them.

Also, some of these databases contain tools to create customized visualizations or to manipulate the data, like the Human Development Report.

Otherwise, there are a couple of tools I’m seriously considering:

  • GSS (via Survey Documentation and Analysis, Berkeley… thanks to Jay Livingstone for mentioning this to me)
  • Statwing (I’m still on the fence on this one because even though it looks like a neat statistical software that is actually affordable, the visualization part lacks options to customize and you might end up having to download things back to Excel, which I’d rather avoid. I wish the software did the whole “upload / visualize / analyze / publish” thing. But I need to work more on this)
  • Health Data Interactive

As much as I can, I only integrate a data analysis component to my introduction to sociology classes. I am not trying to do anything really complicated but I want my students to get a very basic taste of what it means to think with data. For a long time, I had the perfect tool at hand in the form of Microcase Workbooks. There were several of them (a couple for introduction, one for marriages and families, one for social research). MicroCase is a bare bone version of more commons statist8ical software in the social sciences. It is a small program (does not take too much space on your hard drive) that runs on Windows only. However, it uses the GSS, American Community Survey, the World Value Survey and gives students the opportunity to select their own variables, construct their own tables / maps / pie charts / scatterplots / time lines. Students have always found it easy to use and actually fun. Well, that is over as the publisher decided to no longer update the software or the databases. Since then, I have been looking for alternatives. And, of course, publishers’ reps have been more than eager to try to sell me on their latest tools… which are all inadequate for my own purpose. And sending intro students into SPSS is out of the question… heck, I don’t want to go into SPSS.

So, what is a SocProf supposed to do? Well, there are now tons of data and databases that are publicly available. Why not create my own exercises? It will be cheaper to my students and my exercises can be exactly the way I want them. There are also now a lot of visualizing tools, either directly provided by the same organizations that make the data available (like the UN development report or Gapminder). I don’t get dependent upon the good will of a corporate publisher to keep on updating a product that is going to be costly to students. Win-win. On the losing side, it is going to be time-consuming to build up these exercises. I just spent an hour cleaning up data from the CDC on suicide in the US. And it the visualization tools are not available, I can always use Tableau.

So, for instance, indeed, I started simple with some data on suicide in the US. The CDC was the organization with the most data on that. Starting with this:

Suicide Map 1

The first problem with this map is that it is not interactive and the level of detail (by county) makes it a bit busy even if you can clearly regional patterns. These regional patterns actually make for an interesting puzzle for my students to solve. That can be a starting point but it is hard to create rankings, for instance.

A second option is to use the CDC interactive tool through WISQARS. So, basically, it looks like this:

Suicide CDC Interactive 1

As you can see above, you have a series of menus, drop down and radio buttons. You can filter things out. I kept the entire US but I selected “suicide” for intent of injury. And I kept the largest spread (2000 – 2006). I kept all the demographic subset at default. And I  got this as a result:

Suicide CDC Interactive 2

Several problems, with this: (1) on the right hand side, it says “Hover over a state with your mouse to see its name and rate”… that does not work. I tried different browsers including *gasp* Explorer, and no dice. (2) The export data function creates a csv file that takes a lot of cleaning up if you want to do the most simple statistical operations and visualizations. Which is what I ultimately ended up doing in Tableau Public (sorry, the embed still does not work).

The map, though, shows the same pattern as the county one above.

Third option, if you really want an interactive map, and still from the CDC, there is another interactive tool that is a bit trickier to manipulate but does the job: Health Data Interactive:

Suicide CDC Interactive 3

Again, you get to set your options and get an interactive map (with some missing data and only 44 states reporting, which is kinda annoying).

Beyond maps, though, the CDC has some good data visualizations but again, the raw data are harder to track down. For instance, you can get a broad overview over time:

Suicide Overall

Again, you can set up some interesting questions regarding the shifts in age categories with the highest suicide rates, when the shift happened and why. But you can drill down even further and consider race and ethnicity:

Suicide Race Ethnicity

Why whites and American Indian / Alaskan Native / Pacific Islanders (from my little Tableau thing, we already know that Alaska has a high rate)?

Ok, let’s add sex into the mix:

Suicide Race Ethnicity Sex

Across the board, men are way more likely to commit suicide than women. Adding sex does not alter the racial / ethnic patterns. So, should we pity white men after all?’

Finally, let’s add age. Let’s start with the 10-24 age category:

Suicide Age 10-24

One can only ask, what is going on with young American Indian / Alaskan Native / Pacific Islanders? Whites are no longer strikingly higher than other racial and ethnic category, for that age category.

But once you move up the age ladder, into the 25 – 64 category:

Suicide Age 25-64

Then, whites pop up again in the higher rates.

Ok, how about 65 and older:

Suicide Age 65 over

See what happens with American Indian / Alaskan Native / Pacific Islanders? And Whites?

Ok, how about some trends?

Suicide gender trend

Note the uptick with the recession. Otherwise, a familiar gender pattern.

Let’s separate men and women and compare by age categories, first, for men:

Suicide trend males age

The interesting trend here is the progressive joining of the 25-64 (up) and the 65+ (down).

Now, women:

Suicide trend females age

Now, we already know that women are much less likely to commit suicide than men. And this visualization has an extra age category but one can see that the relative increase is greater for women than men. This is especially the case in the 45-54 category.

And now, for the fun of a different visualization, let’s add yet another variable: the means of suicide:

Suicide mechanisms

I am normally not a big fan of stacked bars, but in this case, I think it works. You can clearly see that men are more likely to use a firearm in all age categories whereas suffocation and poisoning are more used by women. One could explore access and cultural factors in the decision to use one mechanisms or another to kill oneself.

This gender aspect is more visible if one filters out other variables:

Suicide mechanisms gender

So, as you can see, there is a lot to explore and a lot of sociological puzzles to be solved, just by using some very basic data, with limited variables, and just by using publicly available data visualizations.

I’ll continue to share these things as I build them.