New Year’s Resolution: Do they make a difference?

It is time to wish everyone a Happy New Year! As we go through the transition of one year to the next, many people think about New Year’s Resolutions. Do they make a difference? If so, what is the best way to assure a New Year’s Resolution be fruitful?

Approximately 40 – 45% of people establish goals for themselves at the start of the New Year. The most common goals deal with health related issues like healthy eating, physical activity, or limiting unhealthy behaviors like smoking. The next common class of New Year’s resolutions are money related issues … like increasing savings or cutting down on spending or debt. However, a New Year’s Resolution can be on any topic, and there is good reason to believe making resolutions can help a person achieve a goal.

Norcross and others, have studied how people change their behavior, including the role of New Year’s Resolutions. In one study, they spoke with people at the start of the New Year and asked them about changes they hoped to see in the next year. Most people were able to express a personal hope for the following year. However, some of those people actively decided to make a New Year’s Resolution and some did not. After 6 months, the researchers followed up with each group and found that 4% non-resolvers were successfully working towards their desired goal whereas 46% of the resolvers were still actively striving to achieve their stated goal. Thus, making a New Year’s Resolution seems to increase behaviors associated with reaching personal goals.

What besides making a resolution increases goal achievement? Koestner found that self-regulatory strength is associated with goal achievement. Specifically, when a person has a sense that the goal they have established was done so for their own benefit and not as a result of pressure from others, they are more likely to implement a plan to reach that goal.

Now, what does a good implementation plan look like? I return to the tried and true research on expertise by Erikson.

  • Clearly state what we are interesting in achieving.

Remember, this has to be something you want … not something you are doing to appease someone else.

  • Specify a plan of how to achieve it.

You have to have a plan. That plan should include: Who, What, Where, When, and How.

  • Make sure the goal is attainable, yet causes us to stretch.

Select a goal and a plan that will fit with your life the way it really is, not the life you wish you had!

  • Establish a way of assessing our progress toward the goal.

Yes, you have to measure your success, which means you have to have a plan to measure your success. Again, sometimes the best way to do this is to seek out recommendations from experts. Often, though, you will have a clear cut goal that will have a clear cut measure associated with it.

  • Use the information from assessment to making changes to the plan.

Don’t just make measures, if you find the plan isn’t working, make changes based on these measures.

  • Deliberately Practice and revise along the way.

Deliberate practice is very targeted practice designed to help us get better. According to Erikson, deliberate practice individuals focus on developing areas of weakness. When faced with challenges or down right failures, people who have adopted a deliberate practice seem to have a laser focus on the area of weakness, and make targeted effort to bring about improvement. Duckworth and others have termed this behavior, Grit, that is the persistence and passion possessed by an individual directed at reaching a long term goal. Grit enables us to maintain interest and drive despite lack of progress, presence of obstacles and even complete and utter failures.

So, making a New Year’s Resolution can make a difference, but only if your personal goal is one that you want for you. Then, create a plan and be gritty as you deliberately practice toward your goal.

Here is hoping you and your family have a Happy, Healthy, and Grit-filled New Year!

Bonnie

Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087–1101.

Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1997). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100, 363-406.

Koestner. (2008). Reaching one’s personal goals: A motivational perspective focused autonomy. Canadian Psychology, 49, 60 – 67.

Norcross, Mrykalo, & Blagys (2002). Auld lang Syne: Success predictors, change processes, and self-reported outcomes of New Year’s resolvers and non-resolvers. Journal of Clinical Psychology, 58, 397-405.

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Making students fail???

By now, anyone reading this blog has probably figured out the only thing I like more than teaching applied statistics is understanding what makes students learn the material better. My goal is not for them to learn the material long enough for the exam, but so they can actually do what the class is intended to do … apply statistics to find the answers to important questions.

My focus on the cognitive science underlying student success is no surprise to people on my campus. As such, I wasn’t the least bit surprised with a science faculty member contacted me to find out the answer to this question. What is better, direct teaching or forcing students to try to figure out something prior to being taught, then teaching them. The specific topic at hand was helping students to understand the application of mathematics in this particular science discipline. The topic came from a teaching listserve.

I have come across a research article addressing this very topic, the abstract can be found by copying and pasting this:  http://onlinelibrary.wiley.com/doi/10.1111/cogs.12107/abstract

Kapur, M. (2014). Productive Failure in Learning Math, Cognitive Science, 38, 1008-1022.

What Kapur found is that though students learn a great deal from direct teaching, that is providing students with background information, showing them how to calculate a math problem, then having them practice, preferably in class, then out of class as homework, direct teaching may not always be the most effective way of having students learn how to solve problems in mathematics.

Instead of direct instruction,  Kapur found that by providing students with the problem and having them figure out how to solve the problem before being instructed yields better long term learning, and also increases a students’ ability to apply that knowledge to other problems. Prior to  instruction, almost every student fails. Yet there seems to be benefit in the attempt despite the failure.

I have used this very technique for years as has my science colleague I spoke of earlier.

I actually begin when I teach the (arithmetic) mean. By the time a student is in college he or she has calculated many means. It is actually a concepts taught to 8 year olds. What the students haven’t been taught is the formula for mean, at least not that my students seem to remember.  Equally true, they haven’t thought about how the mean works. They just plug in numbers into their calculator and it spits out a number.

What I have them do is write  in word the steps involved. Then, I let them ask me questions about symbols. If they can’t figure out they need the symbol for sum of the observations and total number of observation, I will eventual give them to the students. Either way, they have to create the formula for the mean.

After teaching students the conceptual meaning of the Sum of Squared, they determine the formula and process to find it. Then, I define for students variance, and again they generate the format. Obviously, they are asked to generate the definitional formula.  In each case (which by the way covers several days), only about 3 or 4 students in a class of 40 are actually successful. However, most students, who initially won’t even try and respond to my requests with an “I don’t know” eventually start giving it a shot, and most of the time can get a piece of it correct.  More importantly, students start thinking about statistics as a process and the definitional  formulas as a set directions and explanation.  Statistics begins to make sense to students, but it starts with failure.

The same process works with z-test, and all three t-tests (one sample, independent, and related).

There is no doubt that direct teaching will be faster, but forcing students to think about the underling concepts of applied statistics , even if it results in failure, seems to yield deeper and longer understanding, and after all, isn’t that what we are after?

I hope everyone has a great break and wonderful 2015!

Bonnie

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Qualities of a Master Teacher

Psychologists interested in understanding what constitutes a “Master Teacher” have conducted several studies to identify various traits. One resource can be found at the Society for the Teaching of Psychology’s (STP) resource center (OTRP) and is written by Jeffrey Stowell, from Eastern Illinois University, and Eric Landrum, STP President from Boise State University. This article,  http://teachpsych.org/resources/Documents/otrp/resources/stowell13.pdf , contains 73 different clips of professors teaching who are demonstrating Master Teacher Qualities.

In this article, Stowell and Landrum provide a short but detailed summary of the research in identifying what constitutes a master teacher. They focus on 8 of those qualities.

As I read through their qualities I noticed 3 categories.

The stuff a faculty member has to come to class with in order to be a Master Teacher.

  • Knowledgeable  – if you don’t fully know the material, you can’t teach it. Nothing more needs to be said.
  • Enthusiastic about the topic AND teaching – Enthusiasm translates into actions outside of the classroom like attending conferences, reading journals, conducting research, and seeking out time to think and talk about your discipline and teaching.
  • Creative and Interesting – Though this category has more to do with the delivery of information, let’s face it, coming up with a creative method for teaching and keeping your students interested, especially in applied statistics takes time and effort outside of the classroom. Master teachers want to keep their students intellectually engaged, and come to class ready to do just that.

Qualities associated with having high expectations for student success while still referencing the needs of the students.

  • Realistic Expectations – Professors have to keep their expectations high but still within reach of their students. Those of us teaching in the United States, especially at state sponsored universities or community colleges are teaching students who are coming to us woefully ill prepared. It is our goal is to meet the students where they are, not where we wish them to be. With such expectations, it shapes the efforts a professor will entertain to assure student success.
  • Flexible – it is not enough to have high expectations, Master Teachers reference the needs of the student, that means, they have to be flexible, at least at times.

Qualities that help create a welcoming culture of respect within the classroom.

  • Respectful – If you want students to be respectful, you need to model it yourself.
  • Cares for Students – Dr. Jyh-Hann Chang conducts research on compassion and defines compassion as having two components: empathy and action to alleviate their suffering. It’s not just enough to say you care about students, you have to have empathy for them, particularly when something is out of their control like the death of a loved one. With this, however, I also believe, based on the work of Chang, that Master teachers actively try to keep students from suffering.
  • Personable – Who would you rather learn from, a grumpy person or a pleasant person? Most students would select the pleasant student.

What’s great about Stowell and Landrum’s article from STP’s OTRP is it includes links of professors demonstrating these very traits listed above. If you are wondering if you are meeting the standard of being a master teacher I encourage you to watch the video clips.

Until the next time, happy teaching!

Bonnie

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Great Resource for the Teaching of Applied Statistics

Hello All,

The Society for the Teaching of Psychology has an office dedicated to great, peer-reviewed resources for teaching called the Office of Teaching Resources in Psychology.

Two such (free) resources for those of us teaching applied statistics include the free on-line book, Teaching Statistics and Research Methods: Tips from TOPS. http://teachpsych.org/ebooks/stats2012/index.php

Another such resource, is Statistical Literacy in Psychology: Resources, Activities, and Assessment Methodshttp://teachpsych.org/Resources/Documents/otrp/resources/statistics/STP_Statistical%20Literacy_Psychology%20Major%20Learning%20Goals_4-2014.pdf

The web site housing these two resources is filled with great ideas, all of which have been peer-reviewed. You can find teaching resources including example syllabi as well as article on how to maximize your students’ learning. Even if you are teaching applied statistics in an area outside of psychology, I encourage you to make use of this value set of tools. ( http://teachpsych.org/ )

Happy Teaching!

Bonnie

 

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Starting a new semester … engaging students

Bonnie:

To aid with the start of the semester…

Originally posted on Statistical Sage Blog:

School has started, which is always exciting.  After all, the new school year is filled with GREAT possibilities.

Yet, lets face it … most of the students in our classes this fall aren’t going to have that level of excitement, especially about having to take statistics. I often contemplate, why aren’t students excited about the possibilities of learning statistics? Though I certainly don’t have all of the answers, and look forward to hearing from the sages, I do have a few hypotheses, each that I will be discussing over the course of the next few weeks.

(1) People don’t trust statistics, and the students have heard these comments, possibly for years. http://www.quotegarden.com/statistics.html Take this link to a list of quotes on statistics, and see how many of them basically say … you can use statistics to lie. Of course, it probably doesn’t help labor day marks the start of the big push for…

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First Day of Class — starting off right

Bonnie:

Ah, the first day of the semester is upon us …

Originally posted on Statistical Sage Blog:

How do students comes to us on the first day of class? Yes, I can just about hear you mumbling …

(1) They wonder why they even have to take this class … after all they are a [non-quantitative] major. Why does [psychology, sociology, business, education, etc] need statistics?

(2) They may have had really bad math experiences in the past leading to (a) math anxiety (b) poor math attitudes including a low self efficacy and/or (c) weak math skills.

(3) They have heard lots and lots of stories as to how hard or useless or manipulative statistics can be. We have all heard the quote … and so have they … “There are lies, … , and statistics!”

But the first thing I want to let you know is … instructors of applied statistics may be over estimating the negative thinking of their students. Mills (2004) http://findarticles.com/p/articles/mi_m0FCR/is_3_38/ai_n6249218/?tag=content;col1 found that, in general…

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The Importance of Questions in Data Analysis

“The sexy job in the next ten years will be statisticians” said Google’s Chief Economist back in 2009.  The ability to understand data and pull out valuable insight will become increasingly in demand in business, government, and journalism to name but a few fields.

And one of the most important first steps when analysing data is the questions you ask.

Let’s take journalism as an example.  In years gone by, a researcher would surround himself with the national and regional papers and scour them for hours, searching for one line – a line that begged more questions to be asked.  He’d return to the newsroom from this activity present the line to a journalist, and say “follow this up.”

A brainstorming session would follow.  But these were different from what you and I might think of as a brainstorming session.  It wasn’t sitting around staring at a blank flipchart or whiteboard.  Instead they arrived with the idea.  The goal of the brainstorming session was for numerous people to fire as many questions as they could think of in 10 minutes.  Any more than 10 minutes, and they had probably started naval-gazing.  This was a quick hit to explore as many angles as they could think of.  Then filter them down to the juiciest ones, guided by every journalist’s greatest asset – a nose for a story.

Let’s take a current example.  The European Court of Justice ruled in March 2011 that “taking the gender of the insured individual into account as a risk factor in insurance contracts constitutes discrimination”.  The ruling will come into effect in December 2012.  Insurers have had the time in between to adjust their pricing models.

This ruling poses a number of questions for insurers and the general public.  It also challenges the application of statistics in this context.

Faced with this news, what questions can your class generate?  What if they take on different roles?  What questions might the insurer ask?  How might they adjust their policies to account for the new ruling?  What about a journalist?  What questions might they ask in the public’s interest? What about the perspective of the judge in the ruling?  What might the opposing lawyers have argued?  If you take this ruling further, what implications could there be?  What challenges could you make to the ruling?

The average premium for women in the UK is £425 pa compared with £536 pa for men.  However, what challenges can be made to the use of averages in this instance?  Given the opportunity, how would you dive into the data to gain greater insight?

Some commentary and coverage around this ruling could add additional contextual information and offers the jump-off point for further questions and discussion:

“Currently millions of insurance policies take gender into account. The court ruled that practice as inappropriate since there are myriad other factors that could be considered. Gender, however, is typically easy to check and can point to sound statistical conclusions, the industry says.”  NY Times

Speaking of the case’s advocate general, Julianne Kott, the Wall Street Journal writes:

“Life-insurance discrimination might be permissible under the law, she allows, if women live longer because they are women, if there is something innate and biological about the female sex that causes longevity.”

But, she argues, important causes of longevity are behavioral—eating habits, smoking and drinking, sports, work environments, drug use. That women have, on average, behaved differently than men doesn’t necessarily mean any one woman’s femaleness is the reason why.

Differences in longevity “merely come to light statistically,” Ms. Kokott writes, and sex is thus just shorthand for whatever is causing those differences. And, she says, “the use of a person’s sex as a kind of substitute criterion for other distinguishing features is incompatible with the equal treatment of men and women.””

One suggestion is that insurers might encourage more people to sign up for black box insurance.

Black box insurance – also known as ‘telematics’ or ‘pay as you go car insurance’- aims to offer drivers a cheaper alternative by delivering driver-centred premiums based upon actual driving style rather than statistics.”

Similar in concept to the black boxes in aeroplanes (though presumably not indestructible), these devices track when and where you are driving and measures your speed, acceleration and braking.  Instead of using statistics based on your demographics, it would give a more direct impression of how safe a driver you are.  However, this doesn’t remove statistics completely.  The roads you drive on and the time of night you drive impact how much you have to pay, which presumably is based on the probability of having an accident.

The Guardian discusses other ways insurers might respond to the new ruling:

““It has been suggested some insurers may try to get round the rules by re-classifying the cars typically bought by young men into a higher insurance category, which would in turn push their premiums up. The ABI research paper mentioned an unnamed insurer which said women accounted for 70% of its Mini drivers, but only 30% of its BMW drivers. Alternatively, car insurers may start paying more attention to people’s occupations.”

One suggestion is that insurers might encourage more people to sign up for black box insurance:

Black box insurance – also known as ‘telematics’ or ‘pay as you go car insurance’- aims to offer drivers a cheaper alternative by delivering driver-centred premiums based upon actual driving style rather than statistics.”

As an example exercise, you could divide students into small discussion groups, and assign them roles (e.g. one group could be journalists for national press, another could be journalists of an insurance industry publication, and a third group could be senior managers of an insurance company).

Give them each ten minutes to brainstorm within the group as many different questions as they can.  Then get them to filter down the questions to the most important, and discuss how they might go about answering these questions, and the potential implications of their findings.  Then get each group to report back to the larger group, and invite further questions from the class.

You could then review the whole session and how asking more questions early on has an impact in how you approach statistical analysis, and other contexts in which you could apply this approach.

All of this should hopefully stimulate engaging and lively debate based on a real-world example of applied statistics.

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