# Quantifying racial bias in peremptory challenges

A question came up recently on cross validated about putting some numbers on the amount of bias in jury selection. I had a previous question of a similar nature, so it had been on my mind previously. The original poster did not say this was specifically for a Batson challenge, but that is simply my presumption. It is both amazing and maddening that given the same question four different potential analyses were suggested. Although it is a bit out of the norm for what I talk about, I figured it would be worth a post.

# Some background on Batson

For some background, Batson challenges are specifically in the context of selecting jurors for a trial. (Everything that follows is specific to what I know about law in the US.) To select a jury first the court selects potential jurors for the venire from the general public. Then both the prosecution and defence counsels have the opportunity to question individuals in the venire. A typical flow seems to be that a panel of the venire is selected (say 10), then the court has a set of standardized questions they ask every individual potential juror. This part is referred to as voir dire. If the individual states they can not be impartial, or there is some other characteristic that indicates they cannot be impartial that potential juror can be eliminated for cause. Without intervention of counsel the standard questions by the court typically weeds out any obvious cases. After the standard questions both counsels have the opportunity to ask their own questions and further identify challenges for cause. There are no limits on who can be eliminated for cause.

The wrinkle specific to Batson though is that each counsel is given a fixed number of peremptory challenges. The number is dictated by the severity of the case (in more serious cases each side has a higher number of challenges). The wikipedia page says in some circumstances the defense gets more than the prosecution, but the total number is always fixed in advance.

The logic behind peremptory challenges is that either counsel can use personal discretion to eliminate potential jurors without needing a justification. Basically it is a fail-safe of the court to allow gut feelings of either counsel to eliminate jurors they believe will be partial to the opposing side. But based on the equal protection clause it was decided in Batson vs. Kentucky that one can not use the challenges solely based on race. As a side effect of allowing so many peremptory challenges, one can easily eliminate a particular minority group, as being a minority group they will only have a few representatives in the venire.

During the voir dire if the opposing counsel believes the opposition is using the peremptory challenges in a racially discriminatory manner, they can object with a Batson challenge. The supreme court decided on three steps to evaluate the challenge.

1. The party that objected has the burden to prove a prima facie case that the challenges were used in a discriminatory manner. This includes an argument that the group is discriminated against is cognizable, and that there is additional numerical evidence of discrimination.
2. Then the burden shifts on the party being challenged to justify the use of the peremptory challenges based on race neutral reasons.
3. The burden then shifts back to the original challenging party. This is to dispute whether the reasons proferred for the use of the peremptory challenges are purely pretextual.

Witnessing the proceedings for this particular case in the New York court of appeals case is what prompted my interest, and I recommend reading their decision as a good general background on Batson challenges (the wikipedia page is lacking quite a bit). What follows is some number crunching specific to the first part, establishing a prima facie case of discrimination.

# Now some numbers

Batson challenges are made in situ during voir dire. All the cases I am familiar with simply use fractions to establish that the peremptory challenges are being used in a discriminatory manner. The fact that the numbers are changing during voir dire makes the calculations of statistics more difficult. But I will address the ex post facto assessment of the first step given the final counts of the number of peremptory challenges and the total number on the venire with their racial distribution. This presumption I will later discuss how it might impact on the findings in a more realistic setting.

Consider the case of People vs. Hecker (linked to above). It happened that the peremptory challenges by the defence to exclude two Asian’s from the jury panel is what prompted the Batson challenge. Later on one other Asian juror was seated to the jury. The appeals court considered in this case whether step 1 was justified, so it is not a totally academic question to attempt to quantify the chances of two out of three Asian’s being challenged.

First, I will specify how we might put a number of this chance occurrence. If a person randomly selected 13 names out of a hat with 39 people, and of those names 3 individuals were Asian, what is the probability that 2 of those selected would be Asian? This probability is dictated by the hypergeometric distribution. More generally, the set up is:

• n equals the total number of eligible cases that are subject to be challenged
• p equals the total number of the race in question that are subject to be challenged
• k equals the total number of peremptory challenges used on the racial group in question
• d equals the total number of peremptory challenges

And the hypergeometric distribution is calculated using binomial coefficients as:

$\frac{{p \choose k} {n-p \choose d-k} }{{n \choose d}}$

So plugging the numbers listed above into the formula, we get the probability of two out of three Asian jurors being challenged if the challenges were made randomly would be equal to below according to Wolfram Alpha:

$\frac{{p \choose k} {n-p \choose d-k} }{{n \choose d}} = \frac{{3 \choose 2} {39-3 \choose 13-2} }{{39 \choose 13}} = \frac{156}{703} \approx 0.22$

So the probability of a chance occurrence given this particular set of circumstances is 22%, not terribly small, although may be sufficient given other circumstances to justify the first step (it seems the first step is intended that the burden is rather light). Where did my numbers come from though exactly? Given the circumstances of the case in the appeal decision the only number that would be uncontroversial would be p = 2, that two Asian jurors were excluded based on peremptory challenges. p = 3 comes from after the case, in which one other Asian juror was actually seated. It appears in the appeals case I linked to they consider the jury composition after the Batson challenge was initially brought, but obviously the initial trial judge can’t use that future information. I choose to use d = 13 because the defence only used 13 of their 15 peremptory challenges by the end of the seating. The total number n = 39 is the most difficult to come by.

The appeal case states that in the first pool 18 jurors were brought for questioning, 5 were eliminated for cause, and that both the defence and prosecution used 5 peremptory challenges. I chose to count the total number as 13 for this round, 18 minus the 5 eliminated for cause. In our pulling names out of the hat experiment though you may consider the number to be only 8, so not count the cases the prosecution used their peremptory challenges on. The second round brought another 18 potential jurors, of which 4 were eliminated for challenge. In this round the two Asian jurors were challenged by the defence when the judge asked to evaluate the first 9 of this panel (given the language it appears defence used 3 peremptory challenges during this evaluation of the first 9). At the end of the second round both parties used another 5 peremptory challenges. So to get the the total number of 39, I use 13 for the first round plus 14 for the second, although I could reasonably use 8 for the first and 9 for the second. I end up at 39 by using 13 + 14 + 12 – the last Asian juror (Kazuko) was the the 13th to be questioned on the third panel. One prior juror had been challenged for cause, so I count this as 12 towards the total of 39. There were a total of 26 panellists in this round, and the defence used a total of another 3 peremptory challenges, and there is no other information on whether the prosecution used any more peremptory challenges. (I’m unsure the total number of jurors seated for the case, so I can’t make many other guesses – the total number of jurors selected in the prior two rounds were 7). I don’t worry about the selection of the alternate juror for this analysis.

So lets try to apply this same analysis at the exact time the Batson challenge was raised, when the second Asian juror was eliminated. I prefer to make the calculations at the time the challenge was raised, as the opposing counsel may later alter their behavior in light of a prior Batson challenge. In doing this, now we have p = 2 and k = 2 (there was no other mention of any Asian’s being challenged for cause). We have a bit of uncertainty about d and n though. d at a minimum for the defence has to be 7 at this point (five in the first round plus the two Asians in the second round), but could be as many as 10 (5 in the first and 5 in the second). n could be as mentioned before 8 + 9 = 17 (excluding cases the prosecution challenged) or 13 + 14 = 27 (including all cases not challenged for cause). In the middle you may consider n to be 13 + 9 = 22, the exact point when the defence was asked to bring forth challenges for the first 9 seated on that round of the panel. So what difference does this make on the estimated probabilities? Well lets just graph the estimates for all values of d between 7 and 10, and n between 17 and 27. Here the lines are for different values of d (with labels at the beginning left part of the line), and the x axis is the different values of n. We can see the probabilities follow the pattern that as n increases and d decreases the probability of that combination goes down. Even over all these values the probability never goes below 5%. I do not know if a 5% probability is sufficient for the numerical justification of the prima facie case of discrimination. 22% seems too low a threshold to me (by chance about 1 in 5 times) but 5% may be good enough (by chance 1 in 20 times).

So lets try this same sensitivity analysis for the entire case. For the evaluation of everything after the fact I think p = 3 and k = 2 are largely uncontroversial, but lets vary d between 7 and 15 and n between 23 (chosen to be at the lower end of cases that were legitimately evaluated in the pool by the end I believe) to 45. Some of these situations are not commensurate with the limited information we have (e.g. d = 7 and n = 45 is not possible) but I think the graph will be informative anyway. My labelling could use more work, but the line on top is d = 15 and they increment until the line on bottom where d = 7.

So here we can see that the probability after the fact never gets much below 10%, and that is for lower values of d and higher values of n that are not likely possible given the data. So basically in the case that looks the worst for the defence here the probability of selecting two out of three Asian’s by random (giving varying numbers of peremptory challenges and varying the pool from which to draw them) is never below 10%. Not a terribly strong case that the numerical portion of step 1 has been satisfied. Basically, no matter what reasonable values you put in for d or n in this circumstance the probability of choosing 2 out of three Asian jurors randomly is not going to be much below 10%.

# On some of the other suggested analyses

On the original question on CV I mentioned previously, besides my own suggested analysis here there were 3 other suggested analysis:

• Using a regression model to predict the probability of a racial group being challenged
• Analysis of Contingency tables
• Calculating all potential permutations, and then counting the percentage of those permutations that meet some criteria.

All three I do not think are completely unreasonable, but I prefer the approach I listed above. I will attempt to articulate those reasons.

So first I will talk about the regression approach. This is generically a model of the form predicting the probability of a peremptory challenge based on the race of the potential juror:

$\text{Prob}(\text{Challenge}) = f(\beta_0 + \beta_1(\text{Racial Group}_i))$

The anonymous function is typically a logit (for a logistic model) or a probit function. This generalized linear model is then estimated via maximum likelihood, and one formulates hypothesis tests for the B_1 coefficient. The easy critique of this is that the test is not likely to be very powerful with the small samples – as the estimates are based on maximum likelihood and are only guaranteed to unbiased asymptotically. This could be a fairly simple exercise to attempt to see the behavior of this bias in the small samples, but I suspect it reduces the power of the test greatly. Also note that in the case where the subgroup of interest is always challenged, such as in the two out of two Asian’s in the Hecker case mentioned, the equation is not identified due to perfect separation. There are alternative ways to estimate the equation in the case of perfect separation, but this does not mitigate the small sample problem.

More generally, my original formulation of the data generating mechanism being the hypergeometric distribution, drawing names out of hat, is quite different than this. This is a model of the probability of anyone being peremptory challenged. One then estimates the model to see if the probability is increased among the racial group of interest. This is arguably not the question of interest. For instance, say the model estimated the probability of an Asian being challenged to be only 6%, and the probability of anyone else to be 4%. In one sense, this establishes the prima facie case of discrimination of Asian’s compared to everyone else, but does only a probability of 6% of using a peremptory challenge warrant a Batson challenge? I don’t think so. If you think that a challenge will never come with such low probabilities, you are right in that the expected probabilities for the racial group of question will not be that low when a Batson challenge is made, but once you consider the uncertainty in the estimates (e.g. 95% confidence intervals) they could easily be that low. On the flip side if the racial group is struck 96% of the time, but everyone else is struck 94% of the time, does that establish the numerical evidence of discrimination? I’m not sure, it may if this prevents any of the particular racial group being seated.

The analysis of contingency tables, in particular Fisher’s Exact Test, is exactly the same as my hypergeometric approach if one only considers the racial group of interest against all other parties. Fisher’s exact test is a reasonable approach over the more typical chi-square because, 1) the cells will be quite small, and 2) this is one of the unusual cases where the marginals are fixed. So making a 2 by 2 contingency table based on the very first example I gave (which resulted in a probability of around 22%) would be a table:

Challenge No Challenge Total
Asian 2 1 3
Other 11 25 36
Total 13 26 39

For the formula the 2 by 2 table is referred to as:

Challenge No Challenge Total
Asian a b a + b
Other c d c + d
Total a + c b + d n

Which Fisher’s Exact Test can be formulated by the binomial coefficients:

$\frac{{a + b \choose a} {c+d \choose c} }{{n \choose a+c}} = \frac{{2 + 1 \choose 2} {11+25 \choose 11} }{{39 \choose 13}}$

Which if you look closely is exactly the same set of binomial coefficients for the hypergeometric test I listed previously. So, as long as one only tests the one racial group against all others Fisher’s Exact test of a 2 by 2 contingency table is exactly the same as my recommendation. I don’t particularly think the historical p-value <= 0.05 standard is necessary, but it is the same information.

What bothers me more about this approach is when people start adding other cells in the contingency table. In the first step you need to establish a pattern of discrimination against one particular cognizable group. The treatment of other groups is non sequitur to this question in the first step. (In People vs Black evaluations of how unemployment was treated for non-black jurors was considered is steps 2 and 3, but not in the first step.) Including other groups into the table though will change the outcome. Such ad hoc decisions on what racial groups to consider should not have any effect on the evidence of discrimination against the specific racial group of interest. This problem of what groups is similarly applicable to the regression approach mentioned above. Hypothesis tests of the coefficients will be dependent on what particular contrasts you wish to draw and will change the estimates if certain groups are specified in the equation.

The final approach, counting up particular permutations that meet a particular threshold is intuitive, but again has an ad hoc element, the same problem with choosing which racial groups will impact the test statistic. All of the approaches (including my own) need to be explicit about the groups being tested beforehand. Only monitoring one group as in the hypergeometric test I presented earlier is much simpler to justify ex post facto, but it still would be best to establish the cognizable groups before voir dire takes place. For the tests that use other racial or ethnic groups in the calculations are much more suspect to justification, as the picking and choosing of the other groups will impact the calculations.

# Some recommendations

A typical question I get asked as an academic is, So what would you recommend to improve the situation? Totally reasonable question that I often don’t have a good answer to. It is easy to throw out recommendations without considering the entirety of the situation, and the complexities of the criminal justice system are no exception. With full awareness that no one with any authority will likely read my recommendations, my suggestions follow none-the-less.

The first is, only slightly in jest, is to only allow 1 peremptory challenge. There is no bright line rule on numerical evidence presented that is necessary to establish discrimination, but the NY State court of appeals case I mentioned did indicate that it takes more than 1 challenge to establish a pattern of discrimination. This may seem extreme, but the logic applies to the same to allowing fewer peremptory challenges. The fewer the challenges, the less capability either counsel has to entirely eliminate a particular racial group from the jury. It simultaneously makes counsels use of the challenges more precious, so they should be more hesitant to use them based on gut feelings predicated solely by racial stereotyping.

As another side effect (good or bad depending on how you look at it) it also makes the evaluation of whether one is using the challenges in a racially discriminatory manner much clearer. As I shown above, when d decreased the probability of the outcome generally decreased. For example with the Hecker case, pretend there were only a total of 5 peremptory challenges. So in this hypothetical situation have n = 20, d = 5, and p and k = 2 (if the number of n was much higher with the number of peremptory challenges limited to 5 for each side the jury would have to be close to set already by the time 20 individuals were questioned). The probability of this is 5%, whereas if d = 7 the probability is 11%. To be very generic, having fewer challenges makes particular racial patterns less likely by chance.

I understand the motivation for peremptory challenges, but it is unclear to me why such a large number are currently afforded for most cases. Also to the extent that timeliness is a priority for the court, allowing fewer challenges would certainly decrease the necessary time needed for voir dire. (Which was a concern in the Hecker case, as the court only allowed a very short time for questioning the panels.)

The second is that case-law should be established for cognizable groups, particularly given the racial make up of the defendent(s) and victim(s). Or, conversely, counsels should be required a priori to voir dire to establish the cognizable groups. This avoids cherry picking any group for a Batson challenge, as one could always specify a group based on the ex post facto characteristics of the groups used for peremptory challenges. To put a probability to whether a certain number of a particular group could be chosen at at random it is necessary to supply the hypothesis before looking at data. Ad hoc selections of a group could always occur, and with some of the other statistical tests ad hoc inclusion of cells in a contingency table or to include in a test statistic could impact the analysis. Making such a case a priori should prevent any nefarious manipulation of the numbers after the fact.

Neither of these appear to be too onerous to me to be reasonable suggestions. I doubt any lawyer or judge is going to be typing binomial coefficients into Wolfram Alpha during voir dire anytime soon though. Maybe I should make a look up table or nomogram for hypergeometric probabilities for typical values that would come up during voir dire. It would be pretty easy for a lawyer to keep a tally and then do a look up, or keep in mind before hand at what point a set of challenges is unlikely due to chance. With many peremptory challenges and few uses of a particular group, I suspect the probabilities of that happening by chance are much larger than people expect. The two out of two Asian’s in the Hecker case is a good example where the numerical evidence of discrimination is very weak no matter how you plug in the numbers.

# The week at Stackexchange 5/21/2013 Edition

Posts I’ve found interesting during the week (or likely over longer periods!) at various forums I participate at.

# SPSS Nabble Group

Hopefully I get more time to blog in the near future, but currently busy, busy, busy! Working on visualizing JTC flow data (presenting at ASC this fall), getting everyone to approve my prospectus, and I have a few more SPSS blog posts I have in mind (restricted cubic splines, visually weighted regression, and using Ripley’s K to analyze temporal crime sprees!)

# The week at Stackexchange 4/28/13 Edition

I know several individuals have blog posts in which they list interesting other articles that happened during the week. Mine will be a bit of a different twist though. I participate in a few of the stack exchange sites (and the SPSS Nabble forum), and often I think a question is interesting, but don’t follow along closely enough to see an answer given. Another situation that happens is I give an answer, and I don’t see other answers to the same post. To help me, and bring greater attention to various posts I find interesting, I figured I would create a weekly listing of those particular questions (no guarantee the question had anything to do with the previous week – I’ll try not to be redundant though!)

## Others

I’ve just noted this because I’ve seen a ton of nice ggplot2 examples from the Didzis Elferts recently on stackoverflow.

# Some theory behind the likability of XKCD style charts

Recently on the stackexchange sites there was a wave of questions regarding how to make XKCD style charts (see example above). Specifically, the hand-drawn imprecise look about the charts.

There also appear to be a variety of other language examples floating around, like MATLAB, D3 and Python.

What is interesting about these exchanges, in some highly scientific/computing communities, is that they are excepted (that was a weird Freudian slip) accepted with open arms. Some dogmatic allegiance to Tufte may consider these to be at best chart junkie, and at worst blatant distortions of data (albeit minor ones). For an example of the fact that at least the R community on stackoverflow is aware of such things, see some of the vitriol to this question about replicating some aesthetic preferences of gradient backgrounds and rounded edges (available in Excel) in R. So what makes these XKCD charts different? Certainly the content of the information in XKCD comics is an improvement over typical horrific 3d pie charts in Excel, but this doesn’t justify there use.

Wood et al. (2012) provide some commentary as to perhaps why people like the charts. Such hypothesis include that the sketchy rendering evokes some mental model of simplicity, and thus reduces barriers to first interpreting the image. The actual sketchy rendering also makes one focus on more obvious global characteristics of the graphic, and thus avoid spending attention on minor imperceivable details. This should also lead into why it is a potentially nice tool to visualize uncertainty in the data presented. The concept of simplifying and generalizing geographic shapes has been known for awhile in cartography (I’m skeptical it is much known in the more general data-viz community), but this is a bit of a unique extension.

Besides the implementations noted at the prior places, they also provide a library, Handy for making sketchy drawings from any graphics produced in Processing. Below are two examples.

So there isn’t just a pretty picture behind the logic of why everyone likes the XKCD style charts. It is a great example of the divide between classical statistical graphics (ala Tufte and Cleveland) versus current individuals within journalism and data-viz who attempt to make charts aesthetically pleasing, attention grabbing, and for the masses. Wood and company take great lengths to show the relative error in the paper cited when using such sketchy rendering, but weighing the benefits of readability vs. error in graphics is a difficult question to address going forward.

# My posts on CrossValidated Blog

I’ve made several guest posts on the (now) currently dormat Cross Validated Community Blog. They are;

The notes on making tables is IMO my most useful collection, with the post on small multiples coming in second. Other contributions currently include;

For those not familiar, Cross Validated is a stackexchange website where one can ask and answer various questions related to statistics. They are a large improvement over list-serve emails, and I hope to promote their usefulness and encourage individuals to either contribute to current forums and/or start new ones for particular areas. I also particpate on the GIS and Academia sites (as well as programming questions for SPSS and R on stackoverflow).

The blog is just an extension of the site, in that Q/A sessions are not well suited for long discussions. So instead of fitting a square peg in a round hole at times, I believe the blog is a useful place for discussions and greater commentary useful for the communities that aren’t quite well placed in Q/A. Unfortunately, community up-take in the blog has been rather minor, so it is currently dormant. Feel free to stop by the Cross Validated chat room Ten fold if you are interested in contributing. I hope to see the blog not die, but IMO there isn’t much point in any of the current people to continue to contribute unless there becomes greater community contribution from other individuals.

# Not participating in SPSS google group anymore, recommend using other SPSS forums

I have participated in asking and answering questions at the SPSS google group for close to two years now. I am going to stop though, as I would recommend going other places to ask and answer questions. The SPSS google group has become over-ridden with spam. Much more spam than actual questions, and I have spent much more time for at least a few months marking spam rather than answering questions.

I have diligently been marking the spam that arises for quite some time, but unfortunately google has not taken any obvious action to prevent it from occurring. I noted this mainly because the majority of recent spam has repeatedly come from only two email addresses. It is sad, mainly because I have a gmail account and I am fairly sure such obvious spam emails would not make it through my gmail account, so I don’t know why they make it through to google groups.

I believe amounts of spam and actual questions have waxed and waned in the past, but I see little reason to continue to use the forum when other (better) alternatives exist. Fortunately I can recommend alternatives to ask questions related to conducting analysis in SPSS, and the majority of the same SPSS experts answer questions at many of the forums.

Second I would recommend CrossValidated if it has statistical content, and Stackoverflow if it is strictly related to programming. SPSS doesn’t have many questions on StackOverflow, but it is one of the more popular tags on CrossValidated. These have the downside that the expert community that answers questions related to SPSS specifically is smaller than Nabble (although respectable), but it has the advantage of a potential greater community input on other tangential aspects, especially related to advice about statistical analysis. Another advantage is the use of tags, and the ability to write posts in markdown as well as embed images. A downside is you can not directly attach files.

Third I would recommend the SPSS developerworks forum. There has been little to no community build up there, and so it is basically ask Jon Peck a question. For this reason I would recommend the other forums over the IBM provided forum. While the different sub sections for different topics is a nice idea, the Stackoverflow style of tags IMO works so much better it is hard to say nice things about the other list-serve or forum types of infrastructure. Jon Peck answers questions at all of the above places as well, so it is not like you are missing his input by choosing stack overflow as opposed to the IBM forum.

I hope in the future the communities will all just pick one place (and I would prefer all the experts migrate to CrossValidated and StackOverflow for many reasons). But at least no one should miss the google group with the other options available.

# Dressing for success in academic interviews

On the academia stack exchange site a recent question came up about how to dress for academic interviews. The verdict was over-dressing is better than under-dressing. I had come across similar (although somewhat discordant) advice previously, but it is always good to have second opinions.

I will mention the previous blog posts I had come across, as they have good advice for giving academic job talks in general;

I think my advice is just give a talk so awesome no one cares about what you wear! But, take that advice with care, it is coming from a person who not only doesn’t know what he wore yesterday, but has on occasion wore shirts backwards (in public) all day long. I would probably only notice if you showed up dressed like Dilbert in this strip;

If you have other questions related to academia head on over and check out the Academia stack exchange site. Here a few examples of some of my favorite discussions so far;

# Beware of Mach Bands in Continuous Color Ramps

A recent post of mine on the cross validated statistics site addressed how to make kernel density maps more visually appealing. The answer there was basically just adjust the bandwidth until you get a reasonably smoothed surface (where reasonable means not over-smoothed to one big hill or undersmoothed to a bunch of unconnected hills).

Another problem that frequently comes along with the utlizing the default types of raster gradients is that of mach bands. Here is a replicated image I used in the cross validated site post (made utilizing the spatstat R library).

Even though the color ramp is continous, you see some artifacts around the gradient where the hue changes from what our eyes see as green to blue. To be more precise, approximately where the green hue touches the blue hue the blue color appears to be lighter than the rest of the blue background. This is not the case though, and is just an optical illusion (you can even see the mach bands in the legend if you look close). Mark Monmonier in How to Lie with Maps gives an example of this, and also uses that as a reason to not use continous color ramps (also another reason he gives is it is very difficult to map a color to an exact numerical location on the ramp). To note this isn’t just something that happens with this particular color ramp, this happens even when the hue is the same (the wikipedia page gives an example with varying grey saturation).

So what you say? Well, part of the reason it is a problem is because the artifact reinforces unnatural boundaries or groupings in the data, the exact opposite of what one wants with a continuous color ramp! Also the groupings are largely at the will of the computer, and I would think the analyst wants to define the groupings themselves when disseminating the maps (although this brings up another problem with how to define the color breaks). A general principle with how people interpret such maps is that they tend to form homogenous groupings anyway, so for both exploratory purposes and disseminating maps we should keep this in mind.

This isn’t a problem limited to isopleth maps either, the Color Brewer online app is explicitly made to demonstrate this phenonenom for choropleth maps visualizing irregular polygons. What happens is that one county that is spatially outlying compared to its neighbors appears more extreme on the color gradient than when it is surrounded by colors with the same hue and saturation. Below is a screen shot of what I am talking about, with some of the examples circled in red. They are easy to see that they are spatially outlying, but harder to map to the actual color on the ramp (and it gets harder when you have more bins).

Even with these problems I think the default plots in the spatstat program are perfectly fine for exploratory analysis. I think to disseminate the plots though I would prefer discrete bins in many (perhaps most) situations. I’ll defer discussion on how to choose the bins to another time!

# Connect with me!

In taking the advice of this question on the Academia stack exchange site, Is web-presence important for researchers?, I’ve created this blog and joined several other social networking sites. If you are interested in my work, or think I would be interested in yours, feel free to connect with me in any of these following venues.

Are there any other sites I should be joining? Let me know in the comments.

# Making a reproducible example in SPSS

Since I participate on several sites in which programming related questions in regards to SPSS appear (StackOverflow and the SPSS Google group forum mainly), I figured it would useful to share some code snippets that would allow one to make a minimal working example to demonstrate what the problem is (similar in flavor to this question on StackOverflow for R).

Basically this involves two steps; 1) making some fake data (or using an existing dataset), 2) including the code that produces the error or a description of what you would like to accomplish. Since 2 will vary depending on your situation, here I will just be demonstrating the first part, making some fake data.

There are four main ways I use syntax to generate fake data on a regular basis to work with. Below I will demonstrate them;

# INPUT PROGRAM

*******************************.
set seed = 10. /* sets random seed generator to make exact data reproducible */.
input program.
loop #j = 1 to 100. /*I typically use scratch variables (i.e. #var) when making loops.
loop #i = 1 to 100. /*multiple loops allows you to make grouped data.
compute V1 = RV.NORM(0,1). /*you can use the random number generators to make different types of data.
compute V2 = RV.UNIFORM(0,1).
compute V3 = RV.POISSON(3).
compute V4 = RV.BERNOULLI(.5).
compute V5 = RV.BINOM(5,.8).
compute mycat = TRUNC(RV.UNIFORM(0,5)). /*this makes categorical data with 4 groups.
compute group = #j. /*this assigns the scratch variable #j to an actual variable.
end case.
end loop.
end loop.
end file.
end input program.
dataset name sim.
execute. /*note spacing is arbitrary and is intended to make code easier to read.
*******************************.


Using an input program block and the random variable functions provided by SPSS is my most frequent way to make data to work with. Above I also demonstrate the ability to make grouped data by using two loops in the input program block, as well as a variety of different data types using SPSS’s random number generator. This is also the best way to make big data, for an example use you can see this question I answered on Stackoverflow, How to aggregate on IQR in SPSS? which required an example with 4 million cases.

# DATA LIST

*******************************.
data list free / V1 (F2.0) V2 (F2.0) V3 (A4).
begin data
1 2 aaaa
3 4 bbbb
5 6 cccc
end data.
dataset name input.
*******************************.


Using data list is just SPSS’s way to read in plain text data. An example where this came in handy was another question I answered on Stackoverflow, How to subtract certain minuted from a DateTime in SPSS. There I read in some custom data-time data as strings and demonstrated how to convert it to actual date-time variables. I also used this recently on a question over at the Developerworks forum to show some plotting capabilities, Plotting lines and error bars. It was just easier in that instance to make some fake data that conformed to how I needed the data in GPL than going through a bunch of transformations to shape the data.

# GET FILE

*******************************.
*Base datasets that come with SPSS.
FILE HANDLE base_data /Name = "C:\Program Files\SPSSInc\Statistics17\Samples\English".
get file = "base_data\Cars.sav".
dataset name cars.
get file = "base_data\1991 U.S. General Social Survey.sav".
dataset name gss.
*there are a bunch more data files in there.
*******************************.


SPSS comes with a bunch of example datasets, and you can insert some simple code to grab one of those. Note here I use FILE HANDLE, making it easier for someone to update their the code to the location of their own data (same for saving data files). Also this logic could be used in an instance if you upload your exact data to say dropbox to allow people to download it.

# Data with cases Python program

*******************************.
begin program.
import statsgeneratedata
numvar = 3
numcase = 100
parms = "0 1 "
dsname = "python_sim"
factor = "nofactor"
corrtype = "ARBITRARY"
corrs = "1 .5 1 .5 .5 1"
displaycorr="noprint"
distribution = "NORMAL"
displayinputpgm = "noprint"
statsgeneratedata.generate(dsname, numvar, numcase, distribution, parms,
factor, corrtype, corrs, displaycorr, displayinputpgm)
end program.
*******************************.


This uses a custom python program makedata available as a download from SPSS developerworks. Although I provide code above, once installed it comes with its own GUI. Although I don’t typically use this when answering questions (as it requires having Python installed) it has certainly come in handy for my own analysis, especially the ability to generate correlated data.

This isn’t the only part of making an easy to answer question, but having some data at hand to demonstrate your problem is a good start. It certainly makes the work of others who are trying to help easier. Also see a (when I am writing this) recent exchange on posting to the NABBLE SPSS group. IMO making some example data to demonstrate your problem is a very good start to asking a clear and cogent question.