
Digital Monarch Watch  Creating the Context Data Analysis 
Creating the Context [Monarch Tagging] Home Research Focus Background Info Research Data Submission Results of Study Data Analysis Conclusion Further Research Guided Research [Vector & Orientation] Research Question Background Info Research Methodology Data Submission Results of Study Data Analysis Conclusion Further Research Research Values Student Research Doing Research Publish View Quick & Easy Map the Wave! Tools Discussion Forum

So, now that I have my data, what do I do with it? Means and Extremes The methods and number of steps used in gathering scientific knowledge may vary from one investigator
to the next, but scientific methods usually involve the alternation of two types of activities,
the observational and the explanatory. So far we have been tagging Monarch butterflies, but we have not yet participated in the explanatory part
of science. We can continue to explore the data we have collected so far to increase the accuracy of
our observations however, the data you downloaded does not really mean very much yet because we do not
know whether this data is typical, high, low, or how it compares to "normal".
We are not even quite sure what "normal" means.
See if you can do a mean for the data on how many monarchs were tagged each day during the fall. Also see if you can find the extremes in your data set  what days had the most tagging activity and which had the least. Visualizing Data Graphs are one way to visualize data and to help the researcher look for patterns. A graph is used to show the relationships of data collected from the experiment. Graphs must be constructed accurately and according to accepted rules. Usually, a graph shows the relationship between two kinds of data. These data are called variables. Time is a very common independent variable. Independent variables are plotted in the horizontal axis, x axis. In the graph below we explore the relationship between date and tagging activity. In this graph date is the independent variable. The dependent variable is sometimes referred to as the outcome variable. The dependent data is plotted on the vertical axis, the y axis. For this graph, number of butterflies tagged, is our dependent variable. Remember when you make graphs;
Spreadsheets will offer you graphing options for your data but it is very important that you understand the graph you have made and that the graph accurately represents your data. The following is a bar graph of date and number of butterflies tagged in 1998. While bar graphs are interesting and a good way to visualize data, they have some problems. This graph does not allow us to really explore the relationship between stomata counts and distance from the Kansas border. (Why not?) Line graphs show the relationship between two kinds of data in which the independent variable is continuos. After the proper points are plotted on the graph, they should be connected by a line. To line more about graphing and how to make various kinds of graphs, the DIGSTATS site should be helpful. The following is a line graph showing date and tagged butterflies and represents the same data presented in the bar graph. Making a Box Plot John Tukey has developed a technique which gives greater prominence to the dispersion, the spread of the data. This method is known as a boxplot, or a boxandwhisker plot. To learn how to construct a boxplot. The following is a boxplot of the data represented in the bar graph and in the line graph. Using Geographic Information Systems for Analysis A geographic information system (GIS) is a computerbased tool for mapping and analyzing things that exist and events that happen on earth. The data that we have collected as a part of this project is well suited to GIS technology because it has a critical geographic dimension. GIS integrates common database operations such as query and statistical analysis with the unique visualization and geographic analysis benefits offered by maps. These abilities distinguish GIS from other types of analysis.
Geographic information systems work with two fundamentally different types of geographic models  the "vector" model and the "raster" model. In the vector model, information about points, lines, and polygons is encoded and stored as a collection of x,y coordinates. The location of a point feature, such as a bore hole, can be described by a single x,y coordinate. Linear features, such as roads and rivers, can be stored as a collection of point coordinates. Polygonal features, such as sales territories and river catchments, can be stored as a closed loop of coordinates. The vector model is extremely useful for describing discrete features, but less useful for describing continuously varying features such as soil type or accessibility costs for hospitals. The raster model has evolved to model such continuous features. A raster image comprises a collection of grid cells rather like a scanned map or picture. Both the vector and raster models for storing geographic data have unique advantages and disadvantages. Modern GISs are able to handle both models. I would like to work with the data in a mapbased format. Using Systems Thinking  Modeling to work with data. Models are an important part of the explanatory part of science. Science is a practical study of what can be observed, and the prediction from that, of what will be observed. Models support moving beyond assimilating content to actually building understanding and effectively sharing this understanding with others. Using modeling software for analysis will build your capacity for, evaluating your models' congruence with reality and seeing complex interdependent relationships. Modeling is another tool of the practicing scientist. The STELLA software used by this project, uses a building block language that allows you to model the system of interest. The structure of virtually any system can be represented using just four icons! Sophisticated mathematics is not required to capture sophisticated relationships, as the STELLA software automatically creates the framework of equations needed to simulate the model. Once the model is constructed, simulations provide the opportunity to test the theories, observe results, and modify assumptions, thereby increasing your understanding of how things really work and how to make them work better. As you explore your data a number of questions will no doubt come to mind. Many of them begin with "Why".......which is good because it means you are ready to really begin the explanatory part of science. This process begins with establishing and refining your questions as a research question . If you do not have much experience with this process the Guided Research will help you begin working in the explanatory part of science. If you ready to jump in on your own go ahead and begin your work. If you need some helpful suggestions for how to proceed, or if you are ready to Publish My Research, this area will help you share the information you develop.

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