Steps to Preparing Data- Keep it Clean!

Collecting pounds of data is useless unless we can do something with it that leads to new knowledge and information. You may start with a mound of useless numbers, samples, and information. It can feel a little overwhelming. You can reduce data anxiety by thinking about your study beforehand and following a few steps to preparing it for analysis and use.

Pre-planning is important. Developing your coding process, organization methods, and statistical measurements beforehand will lead to a better study. That doesn't mean the process are set in stone but that a better data plan improves the end results.

Scrub out useless data that isn't going to help your study. Be aware that what you may find useless does contain useful information. For example, if you have a lot of people who abandon your survey it may be the language, design, or even type of questions that push people to leave.

Take out that data which is truly not helpful to your study because of inaccuracy and human error. Review each removed data points and try and keep some records of what you did. I save multiple versions before and after the scrubbing.

Then I begin to categorize the information about the variables. Sometimes I need to code the data so it is more useful. That occurs when you need a specific numerical number or letter to designate where the data came from. Depending on how I want to categorize I will use any number of methods and coding methodologies.

There is a lot of information out there on classification of data. I suggest you read this blog article from the Digital Guardian Blog. https://digitalguardian.com/blog/what-data-classification-data-classification-definition  It provides some great resources.

Data classification makes data useful. You will want to ensure that whatever encoding process you use that you can find the things you need. I have seem people put together great coding systems and then find they can't retrieve the information from their data bases properly. Keep it simple in science!

Raw data is relatively useless. You have a responsibility to make it more useful by preparing it for data analytics. The better job you do at the root level, the better off you will be when you want to analyze that data for connections and meaning. One of the best things you can do is have this all written down and figured out before you get started.

Boxing and Muay Thai as Fitness: Watch the Knee to Abdomen!

If your planning on getting into shape and don't really know how to do it then you might want to pick up some Muay Thai boxing. While originally a number of years of training in Kenpo and general boxing I must say that Muay Thai has some serious advantages for fitness.

Its almost all cardio!

That is right! Boxers are known to be in shape. They spend almost all of their time training and very little in the ring. Thus, they are constantly conditioning and that means little to no fat on their bodies!

Useful coordination!

When your body works together to complete these moves there is useful coordination. The movements can be used for other types of activities that rely on those basic skills.

Muscle and upper body strength!

Kickboxing is known to produce people with high muscle strength. Mixing speed and force together creates a nuclear powerhouse for growing muscle.

How did I do? Well...it was a great practice. Lots of body mechanic movement type stuff to make sure proper elbow placement and proper rotating of hip during kicks.

Sampling Your Population-One Nut at a Time!

Sampling is a little like going to the grocery store and plucking a nut or two to "sample" the product. While this might get you kicked out of your local grocery store in a research world it is encouraged to sample as much as you want!!!! No one will shun you for grabbing a handful of sample nuts as long as you share what you did! 🤯

It would be in most cases, except a few small populations, excruciatingly difficult to test everyone in the population. We then must consider a smaller and more reasonable size that "sort of" shared the same characteristics as the main population.

The risk in many of these cases is that sampling is done incorrectly and misrepresents the true population. To better ensure accuracy we may use a number different methods such as random and convenience samples. The video below gives a few ideas....




There is no such thing as an accurate sample! You can sample as much as you like, over and over, and it won't be 100% correct! Sampling in different ways by taking multiple measurements from different areas leads to better outcome.

Sometimes samples are not big enough to draw any real conclusions. Confidence levels and sample size calculations can help ensure a size of participants needed to make meaningful conclusions.

Likewise, how we design our study is going to have influence on the samples taken. Designs will impact how and what types of samples are needed and where we draw them from.

What we should learn here is that sampling is very important and if we desire to have studies that draw meaningful conclusions from the data some consideration over sampling is needed. Review your study design, access to samples, the selection and tools you will need to make a solid analysis of your study focus.

Data!!! Data!!! Checking on Secondary Data Sources!

Data can come from all types of sources and piles up in our dashboards and can be of great help or hindrance to the achievement of our goals. If the data isn't managed properly you can make fatal errors that can destroy your business or cost you a lot of money down the road. Because research is expensive most people rely on secondary sources. Secondary sources are cheap but do require a level of care to ensure the data is saying what you think it is saying.

Let us start by saying that secondary data can be expensive! There is a reason why secondary data makes more sense versus inventing and conducting your own research. It can be expensive and time consuming to create new studies. If you don't have the expertise in your business you will likely need outside consultants.

Those businesses that rely on secondary data may want to consider a few issues.....

1. Make sure the data fits your purposes. Secondary data may or may not meet your needs. Just because someone counted the amount of cars it may have no relation to the type of radio those cars use.

2. Check the sources of data. Yes...you need to read the fine print! How the data is collected, organized used and managed is key to deter validity for your study.

3. Look for similar type studies that may support or not support the data collection methods of the study.

You would be amazed but there are times when studies look to create a biased result. This can often happen in political or corporate arenas where a specific outcome has consequences. Academic and government data is more likely to be non-biased when compared to corporate research.

4. Look for non-profits, government, and others who collected data and have a stake in accurate information.

Sometimes the secondary sources are not enough and you must try and build your own knowledge on an ideas. Surveys are often used to help gauge opinions about services and needs. While surveys are generally easy to build if you ask straight forward questions there are all types of caveats and mistakes you can make when you don't have "in house" scientific knowledge. The best bet is to use experts when you plan on using the information for significant investments.

Here is a little advice when determining in house or secondary sources. First try and use secondary sources that provide contextual information about the problem. If there the available information doesn't necessarily answer your question consider an in-house study. Be very sure the answer is worth the time and expense. If you are gauging your customers opinions in a small business just ask them what you want and take notes. If you are poling 20K customers you may want to have someone look at your survey and delivery methods before proceeding.

Industry-Curriculum Alignment Through Skills Assessment

It was great to work with my colleagues presenting a project started a while ago. We found a way to assess market readiness of graduates by using market research mixed with current assessment practices. The methodology worked great for something new! Will likely discuss a little more in the future. :)

Chewy Outdoor U.P. Adventures-Thoughts from a Dog :)

Not sure where he taking me but I hope there are treats!

I know this is silly but I can't keep talking about wildlife, hiking and stuff over and over so I thought I would spice it up a little with thinking about hiking from a dogs angle.....

This is Chewy! He is a sock thief and a convicted potty violator. Despite his shortcomings he loves the outdoors.

We try hunt, hike, cross country run, snow shoe and other stuff as much as we can. This little guy can jump in the water and play around as much as the big dogs.

Must have been an elephant path.

Today's adventure was hiking back on some ATV trails in search of fitness, outdoors, and maybe some small game.

Supporting wild life, habitat restoration, sustainable hunting, pollution clean up, and a lifestyles that allows for outdoor activities is helpful to our lives. There is a part of us that must reach back to our natural state every once in a while to feel more at ease. This is one of the reasons why I love the Upper Peninsula of Michigan!

Refreshing water to drink.

Every time I put one in my mouth I get yelled at!

You can read a little about the U.S. Wildlife Services to learn about what they do. You pay their service through our tax dollars so understand where your money is going and how it helps our environment. https://www.fws.gov/habitat/

Discovering Problems with Exploratory Research

When we think of research an image comes to our mind of the lab coat professor going over calculations, long formulas and test tubes to come up with the next greatest discovery. No need for the coke bottle glasses that move into the finite details of "fineness". Exploratory research may ask a question but doesn't necessary seek a specific answer. Its main goal is to get into the weeds to understand the inner workings of their subject without coming to an pre-conclusions.

Exploratory Research:

When you are not sure exactly what is going on but you got a solid "hunch" you may want to explore ideas and concepts to better understand a problem. It focuses in a broad area and continues to adjust and changes as the situation changes. It is possible to start with one understanding and end with a completely different one later on.

The essential purpose of exploration is to go into the unknown and explore what seems to be an appropriate path of discovery. You really don't know what to expect and instead allow elements to go forward unimpeded so that you can start to see some relations between elements in a way that leads to future research.

Description of Occurrence.

As you explore ideas and concepts you want to include as much descriptive detail as possible. Because you don't know what the elements are you want to record as much pertinent information as you can so as to further your analysis. Sometimes things that seem important today are not so important tomorrow. At some other time you very well may find the opposite to be true.

Describing what is occurring and taking proper notes, recordings, and other documentation helps in the overall evaluation down the road. You will want to document any study you have conducted from start to finish. There should be some logic and method you are following throughout your work and the type of documentation will relate to your design.

There is no Causality in Exploratory Research

There is no causality in exploratory research. We don't know what the variables are but have an idea of where to look for them. While you may end up making some inferential judgments about causality the strength of those connections and whether they are relevant relies on a more quantitative approach.

Don't go into the research with a lot of pre-beliefs and bias or the directions you take will end up leading to false conclusions. The goal is to find new associations among ideas and concepts and not necessarily to prove that existing one's exist. Time again and again the bias of the research makes it into what they are discovering.

Why its not like a Lab?

In a lab you can design studies that manipulate variables. In the field the variables are at the mercy of their environment making cause and effect much more difficult to determine. Exploratory research allows one to determine potential connections while quantitative determines the strength, or insignificance, of those connections.