A helpful structure for analysing graphs

Mathematicians teaching English

“I became a maths teacher so I wouldn’t have to mark essays”
“I’m having trouble getting the students to write down their own ideas”
“When I give them templates I feel as if it’s spoon-feeding them”

These are comments I hear as I visit mathematics teachers who are teaching the new statistics curriculum in New Zealand. They have a point. It is difficult for a mathematics teacher to teach in a different style. But – it can also be rewarding and interesting, and you never get asked, “Where is this useful?”

The statistical enquiry cycle shown in this video provides a structure for all statistical investigations and learning.

We start with a problem or question, and undergo an investigation, either using extant data, an experiment or observational study to answer the question. Writing skills are key in several stages of the cycle. We need to be able to write an investigative question (or hypotheses). We need to write down a plan, and sometimes an entire questionnaire. We need to write down what we find in the analysis and we need to write a conclusion to answer the original question. That’s a whole heap of writing!

And for teachers who may not be all that happy about writing themselves, and students who chose mathematical subjects to avoid writing, it can be a bridge too far.
In previous posts on teaching report writing I promote the use of templates, and give some teaching suggestions.

In this post I am concentrating on analysing graphs, using a handy acronym, OSEM. OSEM was developed by Jeremy Brocklehurst from Lincoln High School near Christchurch NZ. There are other acronyms that would work just as well, but we like this one, not the least for its link with kiwi culture. We think it is awesome (OSEM). You could Google “o for awesome”, to get the background. OSEM stands for Obvious, Specific, Evidence and Meaning. It is a process to follow, rather than a checklist.

The following video takes you a step at a time through analysing a dotplot/boxplot output from iNZight (or R). Through the example, students see how to apply OSEM when examining position, spread, shape and special features of a graph. This helps them to be thorough in their analysis. For the example we use real data. Often the examples in textbooks are too neat, and when students are confronted with the messiness of reality, they don’t know what to say.

I like the use of O for obvious. I think students can be scared to say what they think might be too obvious, and look for tricky things. By including “obvious” in the process, it allows them to write about the important, and usually obvious features of a graph. I also like the emphasis on meaning, Unless the analysis of the data links back to the context and purpose of the investigation, it is merely a mathematical exercise.

Is this spoon-feeding? Far from it. We are giving students a structure that will help them to analyse any graph, including timeseries, scatter plots, and histograms, as well as boxplots and dotplots. It emphasises the use of quantitative information, linked with context. There is nothing revolutionary about it, but I think many statistics teachers may find it helpful as a way to breakdown and demystify the commenting process.

Class use of OSEM

In a class setting, OSEM is a helpful framework for students to work in groups. Students individually (perhaps on personal whiteboards) write down something obvious about the graph. Then they share answers in pairs, and decide which one to carry on with. In the pair they specify and give evidence for their “obvious” statement. Then the pairs form groups of four, and they come up with statements of meaning, that are then shared with the class as a whole.

Spoon feeding has its place

On a side-note – spoon-feeding is a really good way to make sure children get necessary nutrition until they learn to feed themselves. It is preferable to letting them starve before they get the chance to develop sufficient skills and co-ordination to get the food to their mouths independently.

Teach students to learn to fish

There is a common saying that goes roughly, “Give a person a fish and you feed him for a day. Teach a person to fish and you feed her for a lifetime.”

Statistics education is all about teaching people to fish. In a topic on questionnaire design, we choose as our application the consumption of sugar drinks, the latest health evil. We get the students to design questionnaires to find out drinking habits. Clearly we don’t want to focus too much on the sugar drink aspect, as this is the context rather than the point of the learning. What we do want to focus on is the process, so that in future, students can transfer their experience writing a questionnaire about sugar drinks to designing a questionnaire about another topic, such as chocolate, or shoe-buying habits.

Questionnaire design is part of the New Zealand school curriculum, and the process includes a desk-check and a pilot survey. When the students are assessed, they must show the process they have gone through in order to produce the final questionnaire. The process is at least as important as the resulting questionnaire itself.

Here is our latest video, teaching the process of questionnaire design.

Examples help learning

Another important learning tool is the use of examples. When I am writing computer code, I usually search on the web or in the manual for a similar piece of code, and work out how it works and adapt it. When I am trying to make a graphic of something, I look around at other graphics, and see what works for me and what does not. I use what I have learned in developing my own graphics. Similarly when we are teaching questionnaire design, we should have examples of good questionnaires, and not so good questionnaires, so that students can see what they are aiming for. This is especially true for statistical report-writing, where a good example can be very helpful for students to see what is required.

Learning how to learn

But I’d like to take it a step further. Perhaps as well as teaching how to design a questionnaire, or write a report, we should be teaching how to learn how to design a questionnaire. This is a transferable skill to many areas of statistics and probability as well as operations research, mathematics, life… This is teaching people to be “life-long learners”, a popular catchphrase.

We could start the topic by asking, “How would you learn how to design a questionnaire?” then see what the students come up with. If I were trying to learn how to design a questionnaire, I would look at what the process might entail. I would think about the whole statistical process, thinking about similarities and differences. I would think about things that could go wrong in a questionnaire. I would also spend some time on the web, and particularly YouTube, looking at lessons on how to design a questionnaire. I would ask questions. I would look at good questionnaires. I would then try out my process, perhaps on a smaller problem. I would evaluate my process by looking at the end-result. I would think about what worked and what didn’t, and what I would do next time.

This gives us three layers of learning, Our students are learning how to write a questionnaire about sugar drinks, and the output from that is a questionnaire. They are also learning the general process of designing a questionnaire, that can be transferred to other questionnaire contexts. Then at the next level up, they are learning how to learn a process, in this case the process of designing a questionnaire. This skill can be transferred to learning other skills or processes, such as writing a time series report, or setting up an experiment or critiquing a statistical report.

Levels of learning in the statistics classroom

Levels of learning in the statistics classroom

I suspect that the top layer of learning how to learn is often neglected, but is a necessary skill for success at higher learning. We are keen as teachers to make sure that students have all the materials and experiences they need in order to learn processes and concepts. Maybe we need to think a bit more about giving students more opportunities to be consciously learning how to learn new processes and concepts.

We can liken it a little to learning history. When a class studies a certain period in history, there are important concepts and processes that they are also learning, as well as the specifics of that topic. In reality the topic is pretty much arbitrary, as it is the tool by which the students learn history skills, such as critical thinking, comparing, drawing parallels and summarising. In statistics the context, though hopefully interesting, is seldom important in itself. What matters is the concepts, skills and attitudes the student develops through the analysis. The higher level in history might be to learn how to learn about a new philosophical approach, whereas the higher level in statistics is learning how to learn a process.

The materials we provide at Statistics Learning Centre are mainly fishing lessons, with some examples of good and bad fish.  It would be great if we could also use them to develop students’ ability to learn new things, as well as to do statistics. Something to work towards!

Those who can, teach statistics

The phrase I despise more than any in popular use (and believe me there are many contenders) is “Those who can, do, and those who can’t, teach.” I like many of the sayings of George Bernard Shaw, but this one is dismissive, and ignorant and born of jealousy. To me, the ability to teach something is a step higher than being able to do it. The PhD, the highest qualification in academia, is a doctorate. The word “doctor” comes from the Latin word for teacher.

Teaching is a noble profession, on which all other noble professions rest. Teachers are generally motivated by altruism, and often go well beyond the requirements of their job-description to help students. Teachers are derided for their lack of importance, and the easiness of their job. Yet at the same time teachers are expected to undo the ills of society. Everyone “knows” what teachers should do better. Teachers are judged on their output, as if they were the only factor in the mix. Yet how many people really believe their success or failure is due only to the efforts of their teacher?

For some people, teaching comes naturally. But even then, there is the need for pedagogical content knowledge. Teaching is not a generic skill that transfers seamlessly between disciplines. You must be a thinker to be a good teacher. It is not enough to perpetuate the methods you were taught with. Reflection is a necessary part of developing as a teacher. I wrote in an earlier post, “You’re teaching it wrong”, about the process of reflection. Teachers need to know their material, and keep up-to-date with ways of teaching it. They need to be aware of ways that students will have difficulties. Teachers, by sharing ideas and research, can be part of a communal endeavour to increase both content knowledge and pedagogical content knowledge.

There is a difference between being an explainer and being a teacher. Sal Khan, maker of the Khan Academy videos, is a very good explainer. Consequently many students who view the videos are happy that elements of maths and physics that they couldn’t do, have been explained in such a way that they can solve homework problems. This is great. Explaining is an important element in teaching. My own videos aim to explain in such a way that students make sense of difficult concepts, though some videos also illustrate procedure.

Teaching is much more than explaining. Teaching includes awakening a desire to learn and providing the experiences that will help a student to learn.  In these days of ever-expanding knowledge, a content-driven approach to learning and teaching will not serve our citizens well in the long run. Students need to be empowered to seek learning, to criticize, to integrate their knowledge with their life experiences. Learning should be a transformative experience. For this to take place, the teachers need to employ a variety of learner-focussed approaches, as well as explaining.

It cracks me up, the way sugary cereals are advertised as “part of a healthy breakfast”. It isn’t exactly lying, but the healthy breakfast would do pretty well without the sugar-filled cereal. Explanations really are part of a good learning experience, but need to be complemented by discussion, participation, practice and critique.  Explanations are like porridge – healthy, but not a complete breakfast on their own.

Why statistics is so hard to teach

“I’m taking statistics in college next year, and I can’t wait!” said nobody ever!

Not many people actually want to study statistics. Fortunately many people have no choice but to study statistics, as they need it. How much nicer it would be to think that people were studying your subject because they wanted to, rather than because it is necessary for psychology/medicine/biology etc.

In New Zealand, with the changed school curriculum that gives greater focus to statistics, there is a possibility that one day students will be excited to study stats. I am impressed at the way so many teachers have embraced the changed curriculum, despite limited resources, and late changes to assessment specifications. In a few years as teachers become more familiar with and start to specialise in statistics, the change will really take hold, and the rest of the world will watch in awe.

In the meantime, though, let us look at why statistics is difficult to teach.

  1. Students generally take statistics out of necessity.
  2. Statistics is a mixture of quantitative and communication skills.
  3. It is not clear which are right and wrong answers.
  4. Statistical terminology is both vague and specific.
  5. It is difficult to get good resources, using real data in meaningful contexts.
  6. One of the basic procedures, hypothesis testing, is counter-intuitive.
  7. Because the teaching of statistics is comparatively recent, there is little developed pedagogical content knowledge. (Though this is growing)
  8. Technology is forever advancing, requiring regular updating of materials and teaching approaches.

On the other hand, statistics is also a fantastic subject to teach.

  1. Statistics is immediately applicable to life.
  2. It links in with interesting and diverse contexts, including subjects students themselves take.
  3. Studying statistics enables class discussion and debate.
  4. Statistics is necessary and does good.
  5. The study of data and chance can change the way people see the world.
  6. Technlogical advances have put the power for real statistical analysis into the hands of students.
  7. Because the teaching of statistics is new, individuals can make a difference in the way statistics is viewed and taught.

I love to teach. These days many of my students are scattered over the world, watching my videos (for free) on YouTube. It warms my heart when they thank me for making something clear, that had been confusing. I realise that my efforts are small compared to what their teacher is doing, but it is great to be a part of it.

How to learn statistics (Part 2)

Some more help (preaching?) for students of statistics

Last week I outlined the first five principles to help people to learn and study statistics.

They focussed on how you need to practise in order to be good at statistics and you should not wait until you understand it completely before you start applying. I sometimes call this suspending disbelief. Next I talked about the importance of context in a statistical investigation, which is one of the ways that statistics is different from pure mathematics. And finally I stressed the importance of technology as a tool, not only for doing the analysis, but for exploring ideas and gaining understanding.

Here are the next five principles (plus 2):

6. Terminology is important and at times inconsistent

There are several issues with regard to statistical terminology, and I have written a post with ideas for teachers on how to teach terminology.

One issue with terminology is that some words that are used in the study of statistics have meanings in everyday life that are not the same. A clear example of this is the word, “significant”. In regular usage this can mean important or relevant, yet in statistics, it means that there is evidence that an effect that shows up in the sample also exists in the population.

Another issue is that statistics is a relatively young science and there are inconsistencies in terminology. We just have to live with that. Depending on the discipline in which the statistical analysis is applied or studied, different terms can mean the same thing, or very close to it.

A third language problem is that mixed in with the ambiguity of results, and judgment calls, there are some things that are definitely wrong. Teachers and examiners can be extremely picky. In this case I would suggest memorising the correct or accepted terminology for confidence intervals and hypothesis tests. For example I am very fussy about the explanation for the R-squared value in regression. Too often I hear that it says how much of the dependent variable is explained by the independent variable. There needs to be the word “variation” inserted in there to make it acceptable. I encourage my students to memorise a format for writing up such things. This does not substitute for understanding, but the language required is precise, so having a specific way to write it is fine.

This problem with terminology can be quite frustrating, but I think it helps to have it out in the open. Think of it as learning a new language, which is often the case in new subject. Use glossaries, to make sure you really do know what a term means.

7. Discussion is important

This is linked with the issue of language and vocabulary. One way to really learn something is to talk about it with someone else and even to try and teach it to someone else. Most teachers realise that the reason they know something pretty well, is because they have had to teach it. If your class does not include group work, set up your own study group. Talk about the principles as well as the analysis and context, and try to use the language of statistics. Working on assignments together is usually fine, so long as you write them up individually, or according to the assessment requirements.

8. Written communication skills are important

Mathematics has often been a subject of choice for students who are not fluent in English. They can perform well because there is little writing involved in a traditional mathematics course. Statistics is a different matter, though, as all students should be writing reports. This can be difficult at the start, but as students learn to follow a structure, it can be made more palatable. A statistics report is not a work of creative writing, and it is okay to use the same sentence structure more than once. Neither is a statistics report a narrative of what you did to get to the results. Generous use of headings makes a statistical report easier to read and to write. A long report is not better than a short report, if all the relevant details are there.

9. Statistics has an ethical and moral aspect

This principle is interesting, as many teachers of statistics come from a mathematical background, and so have not had exposure to the ethical aspects of research themselves. That is no excuse for students to park their ethics at the door of the classroom. I will be pushing for more consideration of ethical aspects of research as part of the curriculum in New Zealand. Students should not be doing experiments on human subjects that involve delicate subjects such as abuse, or bullying. They should not involve alcohol or other harmful substances. They should be aware of the potential to do harm, and make sure that any participants have been given full information and given consent. This can be quite a hurdle, but is part of being an ethical human being. It also helps students to be more aware when giving or withholding consent in medical and other studies.

10. The study of statistics can change the way you view the world

Sometimes when we learn something at school, it stays at school and has no impact on our everyday lives. This should not be the case with the study of statistics. As we learn about uncertainty and variation we start to see this in the world around us. When we learn about sampling and non-sampling errors, we become more critical of opinion polls and other research reported in the media. As we discover the power of statistical analysis and experimentation, we start to see the importance of evidence-based practice in medicine, social interventions and the like.

11. Statistics is an inherently interesting and relevant subject.

And it can be so much fun. There is a real excitement in exploring data, and becoming a detective. If you aren’t having fun, you aren’t doing it right!

12. Resources from Statistics Learning Centre will help you learn.

Of course!

Statistics is not beautiful (sniff)

Statistics is not really elegant or even fun in the way that a mathematics puzzle can be. But statistics is necessary, and enormously rewarding. I like to think that we use statistical methods and principles to extract truth from data.

This week many of the high school maths teachers in New Zealand were exhorted to take part in a Stanford MOOC about teaching mathematics. I am not a high school maths teacher, but I do try to provide worthwhile materials for them, so I thought I would take a look. It is also an opportunity to look at how people with an annual budget of more than 4 figures produce on-line learning materials. So I enrolled and did the first lesson, which is about people’s attitudes to math(s) and their success or trauma that has led to those attitudes. I’m happy to say that none of this was new to me. I am rather unhappy that it would be new to anyone! Surely all maths teachers know by now that how we deal with students’ small successes and failures in mathematics will create future attitudes leading to further success or failure. If they don’t, they need to take this course. And that makes me happy – that there is such a course, on-line and free for all maths teachers. (As a side note, I loved that Jo, the teacher switched between the American “math” and the British/Australian/NZ “maths”).

I’ve only done the first lesson so far, and intend to do some more, but it seems to be much more about mathematics than statistics, and I am not sure how relevant it will be. And that makes me a bit sad again. (It was an emotional journey!)

Mathematics in its pure form is about thinking. It is problem solving and it can be elegant and so much fun. It is a language that transcends nationality. (Though I have always thought the Greeks get a rough deal as we steal all their letters for the scary stuff.) I was recently asked to present an enrichment lesson to a class of “gifted and talented” students. I found it very easy to think of something mathematical to do – we are going to work around our Rogo puzzle, which has some fantastic mathematical learning opportunities. But thinking up something short and engaging and realistic in the statistics realm is much harder. You can’t do real statistics quickly.

On my run this morning I thought a whole lot more about this mathematics/statistics divide. I have written about it before, but more in defense of statistics, and warning the mathematics teachers to stay away or get with the programme. Understanding commonalities and differences can help us teach better. Mathematics is pure and elegant, and borders on art. It is the purest science. There is little beautiful about statistics. Even the graphs are ugly, with their scattered data and annoying outliers messing it all up. The only way we get symmetry is by assuming away all the badly behaved bits. Probability can be a bit more elegant, but with that we are creeping into the mathematical camp.

English Language and English literature

I like to liken. I’m going to liken maths and stats to English language and English literature. I was good at English at school, and loved the spelling and grammar aspects especially. I have in my library a very large book about the English language, (The Cambridge encyclopedia of the English Language, by David Crystal) and one day I hope to read it all. It talks about sounds and letters, words, grammar, syntax, origins, meanings. Even to dip into, it is fascinating. On the other hand I have recently finished reading “The End of Your Life Book Club” by Will Schwalbe, which is a biography of his amazing mother, set around the last two years of her life as she struggles with cancer. Will and his mother are avid readers, and use her time in treatment to talk about books. This book has been an epiphany for me. I had forgotten how books can change your way of thinking, and how important fiction is. At school I struggled with the literature side of English, as I wanted to know what the author meant, and could not see how it was right to take my own meaning from a book, poem or work of literature.  I have since discovered post-modernism and am happy drawing my own meaning.

So what does this all have to do with maths and statistics? Well I liken maths to English language. In order to be good at English you need to be able to read and write in a functional way. You need to know the mechanisms. You need to be able to DO, not just observe. In mathematics, you need to be able to approach a problem in a mathematical way.  Conversely, to be proficient in literature, you do not need to be able to produce literature. You need to be able to read literature with a critical mind, and appreciate the ideas, the words, the structure. You do need to be able to write enough to express your critique, but that is a different matter from writing a novel.  This, to me is like being statistically literate – you can read a statistical report, and ask the right questions. You can make sense of it, and not be at the mercy of poorly executed or mendacious research. You can even write a summary or a critique of a statistical analysis. But you do not need to be able to perform the actual analysis yourself, nor do you need to know the exact mathematical theory underlying it.

Statistical Literacy?

Maybe there is a problem with the term “statistical literacy”. The traditional meaning of literacy includes being able to read and write – to consume and to produce – to take meaning and to create meaning. I’m not convinced that what is called statistical literacy is the same.

Where I’m heading with this, is that statistics is a way to win back the mathematically disenfranchised. If I were teaching statistics to a high school class I would spend some time talking about what statistics involves and how it overlaps with, but is not mathematics. I would explain that even people who have had difficulty in the past with mathematics, can do well at statistics.

The following table outlines the different emphasis of the two disciplines.

Mathematics Statistics
Proficiency with numbers is important Proficiency with numbers is helpful
Abstract ideas are important Concrete applications are important
Context is to be removed so that we can model the underlying ideas Context is crucial to all statistical analysis
You don’t need to write very much. Written expression in English is important

Another idea related to this is that of “magic formulas” or the cookbook approach. I don’t have a problem with cookbooks and knitting patterns. They help me to make things I could not otherwise. However, the more I use recipes and patterns, the more I understand the principles on which they are based. But this is a thought for another day.

The Knife-edge of Competence

I do my own video-editing using a very versatile and complex program called Adobe Premiere Pro. I have had no formal training, and get help by ringing my son, who taught me all I know and can usually rescue me with patient instructions over the phone. At times, especially in the early stages I have felt myself wobbling along the knife-edge of competence. All I needed was for something new to go wrong, or or click a button inadvertently and I would fall off the knife-edge and the whole project would disappear into a mass of binary. This was not without good reason. Premiere Pro wasn’t always stable on our computer, and at one point it took us several weeks to get our hard-drive replaced. (Apple “Time machine” saved me from despair). And sometimes I would forget to save regularly and a morning’s work was lost. (Even time-machine can’t help with that level of incompetence.)

But despite my severe limitations I have managed to edit over twenty videos that now receive due attention (and at times adulation!) on YouTube. It isn’t an easy feeling, to be teetering on the brink of disaster, real or imagined. But there was no alternative, and there is a sense of pride at having made it through with only a few scars and not too much inappropriate language.

There are some things at which I feel totally competent. I can speak to a crowd of any number of people and feel happy that they will be entertained, edified and perhaps even educated. I can analyse data using basic statistical methods. I can teach a person about inference. Performing these tasks is a joy, because I know I have the prerequisite skills and knowledge to cope with whatever happens. But on the way to getting to this point, I had to walk the knife-edge of competence.

Many teachers of statistics know too well this knife-edge. In New Zealand at present there are a large number of teachers of Year 13 statistics who are teaching about bootstrapping, when their own understanding of it is sketchy. They are teaching how to write statistical reports, when they have never written one themselves. They are assessing statements about statistics that they are not actually sure about. This is a knife-edge. They feel that any minute a student will ask them a question about the content that they cannot answer. These are not beginning teachers, but teachers with years and decades of experience in teaching mathematics and mathematical statistics. But the innovations of the curriculum have put them in an uncomfortable position. Inconsistent, tardy and even incorrect information from the qualification agency is not helping, but that is a story for another day.

In another arena there are professors and lecturers of statistics (in the antipodes we do not throw around the title “professor” with the abandon of our North American cousins) who are extremely competent at statistical mathematics and analysis but who struggle to teach in a satisfactory way. Their knife-edge concerns teaching, appropriate explanation and the generation of effective learning activities and assessments in the absence of any educational training. They fear that someone will realise one day that they don’t really know how to devise learning objectives, and provide fair assessments. I am hoping that this blog is going some way to helping these people to ask for help! Unfortunately the frequent response is avoidance behaviour, which is alarmingly supported by a system that rewards research publications rather than effective educational endeavours.

So what do you do when you are walking the knife-edge of competence?

You do the best you can.

And sometimes you fake it.

I am led to believe there is a gender-divide on this. Some people are better at hiding their incompetence than others, and just about all the people I know like that are men. I had a classmate in my honours year who was at a similar level of competence to me, but he applied for jobs I wouldn’t have contemplated. The fear of being shown up as a fake, or not knowing EXACTLY what to do at any point stopped me from venturing. He horrified me further a few years later when he set up his own company. Nearly three decades, two children and a PhD later I am not so fastidious or “nice” in the Jane Austen meaning of the word. If I think I can probably learn how to do something in time to make a reasonable fist of it and not cause actual harm, I’m likely to have a go. Hence taking my redundancy and running!

When I first lectured in statistics for management,  I did not know much beyond what I was teaching. I lived in fear that someone would ask me a question that I couldn’t answer and I would be revealed as the fake I was. Well you know, it never happened! I even taught students who were statistics majors, who did know more than I, and post-graduate students in psychology and heads of mathematics departments, and my fears were never realised. In fact the stats students told me that they finally understood the central limit theorem, thanks to my nifty little exercise using dotplots on minitab. (Which was how I had finally understood the central limit theorem – or at least the guts of it.)

I’m guessing that this is probably true for most of the mathematics teachers who are worrying. Despite their fear, they have not been challenged or called out.

The teachers’ other unease is the feeling that they are not giving the best service to their students, and the students will suffer, miss out on scholarships, decide not to get a higher education and live their lives on the street.  I may be exaggerating a little here, but certainly few of us like to give a service that is less than what we are accustomed to. We feel bad when we do something that feels substandard.

There are two things I learned in my twenty years of lecturing that may help here:

We don’t know how students perceive what we do. Every now and again I would come out of a lecture with sweat trickling down my spine because something had gone wrong. It might be that in the middle of an explanation I had had second thoughts about it, changed tack, then realised I was right in the first-place and ended up confusing myself. Or perhaps part way through a worked example it was pointed out to me that there was a numerical error in line three. To me these were bad, bad things to happen. They undermined my sense of competence. But you know, the students seldom even noticed. What felt like the worst lecture of my life, was in fact still just fine.

The other thing I learned is that we flatter ourselves when we think how much difference our knowledge may make.  Now don’t get me wrong here – teachers make an enormous difference. People who become teachers do so because we want to help people. We want to make a difference in students’ lives. We often have a sense of calling. There may be some teachers who do it because they don’t know what else to do with their degree, but I like to think that most of us teachers teach because to not teach is unthinkable. I despise, to the point of spitting as I talk, the expression “Those who can, do, and those who can’t, teach.” One day when the mood takes me I will write a whole post about the noble art of teaching and the fallacy of that dismissive statement. My next statement is so important I will give it a paragraph of its own.

A teacher who teaches from love, who truly cares about what happens to their students, even if they are struggling on the knife-edge of competence will not ruin their students’ lives through temporary incompetence in an aspect of the curriculum.

There are many ways that a teacher can have devastating effects on their students, but being, for a short time, on the knife-edge of competence, is not one of them.

Take heart, keep calm and carry on!

Teaching statistical report-writing

Teaching how to write statistical reports

It is difficult to write statistical reports and it is difficult to teach how to write statistical reports.

When statistics is taught in the traditional way, with emphasis on the underlying mathematics the process of statistics is truncated at both ends. When we concentrate on the sterile analysis, the messy “writing stuff” is avoided. Students do not devise their own investigative questions, and they do not write up the results.

Here’s the thing though – in reality, the analysis step of a statistical investigation is a very small part of the whole, and performed at the click of a button or two.

Ultimately the embedding of the analysis back into an investigation should not be a problem. The really interesting part of statistics happens all around the analysis. Understanding the context enriches the learning, transforming the discipline from mathematics to statistics. We can help students embrace the excitement of a true statistical investiation. But in this time of transition, the report-writing aspects are a problem. They are a problem for the learner and for the teacher.

The new New Zealand curriculum for statistics requires report-writing as an essential component of the majority of assessment, particularly at the final year of high school. This is causing understandable concern among teachers, who come predominantly from a mathematical background. I can imagine myself a few years ago saying. “I became a maths teacher so I wouldn’t have to teach and mark essays!” In addition the results from the students are less than stellar, even from capable students. Teachers do not like their students to perform poorly.

All statistics courses should have a component of report-writing, unless they are courses in the mathematics of statistics. The problem here is, like the secondary school teachers in New Zealand, many statistics instructors are dealing with the mathematics more than the application of statistics, and are not confident of their own ability at report-writing themselves. Normal human behaviour is to avoid it. Having taught service statistics courses in a business school for two decades, I have gradually made the transition to more emphasis on report-writing and am convinced that statistical report-writing needs to be taught explicitly, and taught well.

Report-writing is a fundamental and useful skill

For teachers who are uncomfortable with teaching and marking reports, it would be nice to dismiss the process of report-writing  as “not important”. Much of statistics teaching is in a service course, as discussed in my previous blog. It is unlikely that any of these students will ever have to write a report on a statistical analysis, other than as part of the assessment for the course.  So why do we put them and ourselves through this?

You don’t realise whether you understand or not until you try to write it down.

The written word requires a higher level of precision than a thought or a spoken explanation. Your sentences look at you from the page and mock you with their vagueness and ambiguity. I find this out time and again as I blog. What seems like a well thought out argument in my head as I do my morning run, falls to shreds on paper, before being mustered into some semblance of order. It is in writing that we identify the flaws in our understanding. As we try to write our findings we become more aware of fuzzy thinking and gaps in reasoning. As we write we are required to organise our thoughts.

Better critics of other reports

A student who has been required to produce a report of a good standard will be exposed to examples of good and bad reports and will be better able to identify incorrect thinking in reports they read themselves. This is perhaps the most important purpose of a terminal course in statistics. Having said that, it is both heart-warming and alarming to hear from past-students the wonderful things they are doing with the statistics they learned in my one-semester course.

Useful skill for employment

Students need to be able to read and write as part of empowered citizenship. The skill of writing a coherent report in good English is highly sought after by employers, and of great use at university in just about every discipline. It is a transferable skill to many endeavours.

Reports are needed for assessment

On a practical level, if the teacher is going to evaluate understanding they need evidence to work from. A written report provides one form of evidence of understanding.

Report-writing is difficult to teach

Some maths teachers may feel inadequate in teaching “English”, as they see report-writing. They do not have the pedagogical content knowledge in teaching writing that they do for teaching algebra or percentages, for instance. Pedagogical content knowledge is more than the intersection of knowing a subject, and being able to teach in a general sort of way. It is the knowledge of how to teach a certain discipline, what is difficult to learners, and how to help them learn.

Some basic ideas for teaching report-writing

To write at good report you need to understand what is going on, have the appropriate vocabulary, and use a clear structure. Good teaching will emphasise understanding. Getting students to write sentences about output, and sharing them with their peers is a great way to identify misunderstandings. As these sentences are shared, the teacher can model the use of correct technical language. They can say, for instance, “You have the essence correct here, but there are some more precise terms you could use, such as …” Teachers can either give students outlines for reports, or they can give them several good reports and get the students to identify the underlying structure. I am a firm believer in the generous use of headings within a report. They provide signposts for writer and reader alike.

You can see this in my video, Writing up a Time Series Report.

Report-writing requires practice. The assessment report should not be the first report of that type that a student writes. In the world of motivated students with no other demands on their time, it would be great to have them write up one assignment for the practice and then learn from that to produce a better one. I am aware that students tend not to do the work unless there is a grade attached to it, so it can be difficult to get a student to do a “practice report” ahead of the “real assessment.”  There are other alternatives that approximate this, however, which require less input from the teacher. One of these, the use of templates, is explained in an earlier post, Templates for statistical reports – spoon-feeding?

There is nothing wrong with using templates and “sensible sentences”. (not to be confused with “sensible sentencing”, which seems devoid of sense.) There are only so many ways to say that “the median number of pairs of shoes owned by women is ten.” It is also a difficult sentence to make sound elegant. Good reports will look similar. This is not creative-writing – it is report-writing. Sure the marking may be boring when all the reports seem very similar, but it is a small price to pay when you avoid banging your head against the desk at the bizarre and disorganised offerings.

This is but a musing on the teaching of report-writing. Glenda Francis, in  “An approach to report writing in statistics courses” identifies similar issues, and provides a fuller background to the problem. She also indicates that there is much to be done in developing this area of teaching and research. I will be providing professional development in this area over the next month to at least three groups of teachers, and I look forward to learning a great deal from them, as we explore these issues together.

Is statistical enquiry a cycle?

What is the statistical enquiry cycle and why is it a cycle? Is it really a cycle?

The New Zealand curriculum for Mathematics and statistics was recently held up as an example of good practice with regard to statistics. Yay us! In New Zealand the learning of statistics starts at the beginning of schooling and is part of the curriculum right through the school years. Statistics is developed as a discipline alongside mathematics, rather than as a subset of it. There are mathematics teachers who view this as an aberration, and believe that when this particular fad is over statistics will go back where it belongs, tucked quietly behind measurement, algebra and arithmetic. But the statisticians rejoice that the rich and exciting world of real data and detective work is being opened up to early learners. The outcome for mathematics and statistics remains to be seen.

A quick look over the Australian curriculum shows ostensibly a similar emphasis with regard to content at most levels.  The big difference (at first perusal) is that the New Zealand curriculum has two strands of statistics – statistical investigation, and statistical literacy, whereas the Australian curriculum has the more mathematical approach of “Data representation and interpretation”.  Both include probability as another strand.

Data Detective Cycle

In the New Zealand curriculum, the statistical investigation strand at every level refers to the “Statistical enquiry cycle”, shown here, which is also known as the PPDAC cycle. This is a unifying theme and organising framework for teachers and learners.

The data detective poster

The data detective poster

This link takes you to a fuller explanation of the statistical enquiry cycle and its role at the different levels of the school curriculum. Note that the levels do not correspond to years. Click here to see the correspondence. The first five levels correspond to about 2 years each, whereas levels 6,7 and 8 correspond to the final three years of high school. So a child working on level 3 is generally aged about 10 or 11.

As I provide resources to support teaching and learning within the NZ curriculum I have become more aware of this framework, and have some questions and suggestions. I have made a table from which I hope to develop another diagram that students at higher levels can engage with, particularly with regard to the reporting aspects. As this is a work in progress you will have to wait!

Origins

Let’s look at the origins of the diagram and terminology. Maxine Pfannkuch (an educator) worked with Chris Wild (a statistician) to articulate what it is that statisticians do. They published their results in the international statistical review in 1999 and contributed the chapter “Towards an understanding of statistical thinking” in “The Challenge of Developing Statistical Literacy, Reasoning and Thinking”, edited by Dani Ben-Zvi and Joan Garfield. The statistical enquiry cycle has consequently been promulgated in the diagram and description referred to above. There is sound research behind this, and it makes good sense as a way of explaining what statisticians do.

Diagrams

I love diagrams. Anyone who has viewed my videos will know this. I spend a great deal of mental energy (usually while running) trying to work out ways to convey ideas in a visual way that will help people to learn, understand and remember. I also do NOT believe in the fad of learning styles, but rather I believe that all learners will gain from different presentations of concepts. I also believe that it is a useful discipline for a teacher to create different ways of expressing concepts. I am rather fussy about diagrams, however, as our Honours students would attest. I have a particular problem with arrows which mean different things in different places. If an arrow denotes passage of time in one instance it should do so in all instances, or a different style of arrow should be employed.

No way in or out

A problem I have with the PPDAC “Cycle” being a cycle is that it seems to imply that we can come in at any point and that there is no escape. If there is a logical starting point, and the link back to it is not one of process, then that should be indicated. Because the arrows are all the same style in the PPDAC diagram, it is also difficult to see a way out of the cycle. As a learner I would find it a little daunting to think that I could never escape! I am also concerned about understanding in what way does a Conclusion lead to a Problem? Surely the whole point of the word “Conclusion” is that it concludes or ends something?

To me there are at least three linkages between the Problem and the Conclusion. First of all, while in the Problem stage, we need to think about what we want to be able to say in the future Conclusion stage.  We may not know which way our conclusion will go, though we will probably have an opinion, or even a hope! (I am too post-modern in my thinking to believe in the objectivity of the researcher.) For instance we may want to be able to say – There is (or is not) evidence that women own more pairs of shoes than men. Another linkage is that when we write up our conclusion we must refer back to the original problem. And the third linkage comes from a comment Jean Thompson made on my blog about teaching time series without many computers. “Often the answer from a good statistical analysis is more questions”.  One conclusion can lead to a new problem.

I found a similar diagram online which is more sequential, starting with the problem and working vertically through the steps, with a link at the end going back to the beginning. I like this, because it does give an idea of conclusion and moving on, rather than being caught in some endless cycle. The reality for students is that they will generally do some project, which will start with a problem and end with a conclusion. Then they will move on to an unrelated project. It has also been my experience as a practitioner.

In my experience the cyclical behaviour which this diagram portrays is generally more within the cycle than over the whole cycle. For instance one may be part way through the data collection and realise that it isn’t going to work, and go back to the “Plan” stage. Some of these extra loops are suggested in my table.

Reporting

For students at a higher level who are required to write reports, it is difficult to see how the report fits in with the cycle. The “Conclusion” step includes “communication”, which could imply a report. However reports often include most of the steps, particularly when their purpose is to satisfy an assessment requirement.

Existing datasets

It is also difficult to apply the cycle in a non-cynical way to work with existing datasets. Often, in the interests of time and quality control, students are given a dataset. In reality they start, not at the Problem step, but somewhere between the Data step and the Analysis step. In their assessments they are required to read around the topic and use their imaginations to come up with the problem, look at how the data was collected, and move on from there.  This is not always the case, but it is for NCEA level 3 Bivariate Investigation, Time Series analysis and Formal Inference areas (called ‘standards’). The only area where they really do plan and collect the data is in the Experimental Design standard. Might it not be helpful to provide an adapted plan that takes into account these exigencies? Let us be explicit about it rather than coyly pretend that the data wasn’t driving everything?

In general I like the concept of the statistical enquiry cycle, and I am happy that it is providing a unifying theme to the curriculum. However, particularly at higher levels, I think it needs a bit of tweaking, taking into account the experience of teachers and learners.  If it is to hold such an important place in a curriculum that is leading the world, it deserves on-going attention.

Disclaimer

This is a blog and not an academic journal. The ideas I have contemplated need a lot more thought and background reading, but I do not have the time or the university salary to support such a luxury right now. Maybe someone else does!

Shibboleth, Mixolydian, Heteroscedasticity – and Kipling

All areas of human endeavour have specific language. Cricket commentators, art critics and wines buff make this very obvious.

Mixolydian

My son, who is blind, autistic and plays the piano like an angel, is studying Jazz, and I’m helping him. You can read more about this in my other blog Never Ordinary. There is a specific language around Jazz, and I’m not talking about ‘scat’. (Hmm just realised the other meaning for that word!) In the Jazz course they use words like Mixolydian, Chromatisism, Quartal Harmony…  I nod and smile. This language expresses ideas clearly and uniquely and is outside my comprehension. (Mixolydian is based on the Major scale, but with a flat 7. – clearer now?)

Trumpetty yellow, Daffodils, Narcissus

This week there was a statistics list discussion about the meaning of the term “multivariate”. As part of the ongoing discussion, someone suggested that using exact terminology exactly avoids a situation such as saying “I have yellow flowers in my garden with trumpetty bits, that come out in spring and have oniony looking bits in the ground.” This can also be said as “I have daffodils in my garden”.  However it can also be said as “I have Narcissus pseudonarcissus  in my garden”. Each of those phrases expresses the same idea, but with differing clarity or exclusiveness depending on the audience.

Hagley Park Daffodils

Shibboleth

Language can be used to exclude, as well as to inform or communicate. The term “shibboleth” comes from the book of Judges. When the Gileadites wished to find out if people crossing the river were Ephraimites, they would ask them to say the word “shibboleth”. If they said it as sibboleth, they killed them. The Old Testament can be a bit like that. The word “shibboleth” is now used to mean a code word, or knowledge that only a certain culture or group will know. Sometimes it can seem that statistical terms are used so only the initiated will be able to understand.

Virtue and Common Touch

As statisticians, operations researchers and teachers of statisticians and operations researchers we have many different opportunities to select the language we use. We must always be aware of our audience. In the poem, “If”, Kipling encourages people to be able to “…talk with crowds and keep your virtue, Or walk with Kings – nor lose the common touch,” Academics “walk with kings” when they write academic papers, using highly specialised and exclusive language. We need to make sure we do not lose the common touch. At the same time we should “keep our virtue”, and use the correct statistical term when the circumstances arise, making sure that we retain the common touch so that all understand.

Heteroscedasticity

When I use the term heteroscedasticity I am usually doing so for one of two reasons. First, that the data in question has non-constant variance, and I am explaining the concept and technical term to a client, student or colleague. Second, because I really like the word. “Heteroscedasticity” is eight syllables of tongue-twisting goodness! But, really, “non-constant variance” says exactly the same thing, has only six syllables and is easier to understand. I suspect a degree of linguistic snobbery appearing.

Communicating Statistics

Greenfield wrote a paper in 1993, which is still disappointingly relevant today. In “Communicating Statistics” (http://greenfieldresearch.co.uk/papers/Communicating%20stats.pdf) he suggests that statisticians have a great deal to offer the world, and that we aren’t doing a good job of making people aware of that. He was damning of the type of language used in academic publications, which ensure that any potentially useful results are obscured by “prolix and pseudo-objective style”.

This flows over into our consulting endeavours, where the aim should be to communicate rather than exclude. Greenfield gives the example fictionalised in this comic:

Depiction of true event.

Depiction of almost true event. Click to view.

Greenfield’s parting provocative statement was to suggest that statisticians produce more cookery-books and more easy-to-use programs, and encourage their use by everybody who can benefit. These books and programs can carry the message that if they want to do better they should study more and seek the guidance of statisticians.

In closing he says “Our audience, our customers are out there. They need us, even if they do not realise it. We must change our culture, our philosophy, our public relations and our use of language to reach them.”

Greenfield Challenge

I’m not sure I want to be telling you about the Greenfield challenge, as I’m thinking of entering it, and would really like a trip to Ankara for the ENBIS conference. But in pursuit of the greater good, I am putting a link here: The Greenfield Challenge. The blurb explains:

“We would like to encourage you to report immediately whenever you’ve had dealings with non-statisticians – in whichever form (face-to-face, in writing, in form of an audio or video recording, in interactive social media … ) or context (interactions with students, educators, managers and employees of organizations in private and public sectors … ).”

Greenfield even suggests “You might even write a short story or a play.”

Still thinking about that one. I guess there is always “The Goal” to look to for an example. In the meantime I’ll stick to this work of mostly non-fiction, interspersed with opinion and anecdote.

Choose our words

When we use very specific technical terms we need to make sure that they are really necessary. Is there a simpler, and just as accurate way of saying the same thing? If our audience is statisticians, then really we can indulge in specific technical language. But if the audience includes students, non-statisticians and the general public, then we should probably use simpler terms, or at least “gloss”, or say what the word means along with its use. (There was an example of glossing right there!)

I have written earlier about the minefield of statistical terminology, particularly when the statistical word also has an everyday meaning which is not quite the same. Examples of this are “significant”, “random” and “relationship”. The post includes some suggestions for teaching statistical language.

But as well as teachers, we are also communicators, and need to get our message across in the best way possible. It is vital to determine our audience, and make sure we bring them along with us.

I contemplate the new New Zealand curriculum with excitement. Through the efforts of a group of statisticians we are able to inculcate a greater understanding of the essentials of statistics from an early age to much of the population. The role of the statisticians is to help the teachers feel at home in the world of statistics, so that they can invite their students along. These are exciting times. The rest of the world is watching.

Statistical Story-telling with time series data

Statistics is about story-telling.

For people who understand them, graphs tell a story. To the initiated, even a p-value, and some summary statistics can help to tell a story. Part of the role of a statistician is to extract the story from the data. The role of a statistics teacher is to enable students first to recognise that there is a story, then to enable them to tell the story through the tools of analysis and communication.

This idea of statistics as story-telling is explained in an award-winning paper byPfannkuch, Regan, Wild and Horton,Telling Data Stories: Essential Dialogues for Comparative Reasoning, which won  the inaugural Journal of Statistics Education Best Paper Award.

Time series data, especially seasonal time series data, yields its story abundantly. For this reason I changed my mind about the teaching of time series analysis at high school. I used to think that it was far too complex for high school students and should be left to higher education. In a way that is true, but if you stick to the basic concepts, it is a contextually rich area of study.

Time series data is full of little hazards, not the least being auto-correlation. We can use moving averages to take out the bumps and exponential smoothing to be more responsive to more recent data. We can deseasonalise and fit a trend line, predict and then put the seasonality back in. There are weighty (in more ways than one) volumes dedicated to time series analysis and the various discoveries and inventions that have helped us draw meaning from the past and forecast the future.

Because of the inherent complexity of time series analysis, I used to think that time series was not an appropriate part of the high school curriculum.

However, if a storytelling approach is used, backed up by appropriate software, then time series is a wonderful introduction to statistics. It is a good example of modelling, it has clear purpose, and the contexts can be fascinating.

Time series analysis is a clear example of the concept of a model, as there are so many different ways that it is possible to model a set of time series data. In contrast, when you teach linear regression with only one possible predictor variable, on data that is nicely behaved, there is generally one sensible model to use. This gives students the idea that you are trying to find “the right model”. This is not the case with time series, as models change, depending on how we choose to define the model.

Another selling-point for time series analysis is that its main function is forecasting. We all want to have crystal balls that can predict the future. The main reason we study a time series is to understand the patterns of data so that we can project into the future, usually for economic reasons. There is no question of “Why are we doing this, Miss?”, as the purpose of the analysis is self-evident.

There are numerous economic time series available from official statistics sites. In New Zealand I went to Infoshare and in the US there is Economagic.  Some of the series are fascinating. (I like the three peaks per year in jewellery sales in the US – December, February and May.)

Analysis can be difficult, and Excel is hideous for time series graphing and deseasonalising. There has been a free front end for R set up, called iNZight, which enables straight-forward time series analysis. One drawback is that it only allows for one model, which I fear perpetuates the “there is one model” mindset.

But the opportunities for storytelling are there. You can talk about trend, seasonality, variation, the relative contribution of each. As teachers and students are exposed to more and more time series graphs, they are better able to tell stories. The graphs of the seasonal shape are rich with story-telling potential.

To support this we have made four videos about time series analysis, and an app, which is still in the pipeline. We hope that these will help develop the confidence of teachers and students to tell stories about time series data. We also have further quizzes and step-by-step guide to writing up a time series analysis. You can get much of this for free from our Free Resources page on StatsLC.com.

For teachers where there is limited access to computer resources, I have an earlier post with some ideas of how to overcome this problem and emphasise the story in time series data: Teaching Time Series with Limited Computer access.

Understanding Time series analysis

Time Series analysis using iNZight:


How to write up a time series report:

and an example of a time series report (aimed at Year 13 students in New Zealand, but a good general framework for report writing.)