Respond directly to the original poster.  Respond to and explain how your colleague’s codes are similar to or different from yours.  APA format.  In text citations.

The video I chose was from week four submitted by Christen Topper, a doctorate student at Walden. In her video, she acts as an agent for social change by pointing out that in Hong Kong children are becoming disconnected with nature. Hong Kong has grown at a rapid pace and buildings have overtaken the city to the extent that the natural vegetation surrounding the city has become overrun. Christen believed that having a connection to nature is imperative and began a program that allowed kindergarten students to build a micro-garden in an outdoor play space where they can re-establish this relationship with nature. 

CODES

1. Code 1: High Rise Buildings

“High rise buildings are taking over the natural green spaces where children usually play…active to sedentary.”

1. Code 2:Nature Deficit Disorder

“Nature deficit disorder is a real social problem in a big city like Hong Kong.”

1. Code 3: Benefits of Garden

“We witnessed the benefits of the garden immediately.” 

I chose these codes because they were short phrases that conveyed the meaning behind each paragraph. According to Saldana (2016),”Also, be aware that a code can sometimes summarize, distill, or condense data, not simply reduce them.” (p. 5). In other words, each of the codes I utilized conveyed enough meaning to help me understand what the author was trying to communicate. According to Ravitch and Carl (2016),”When coding, include enough text so that the meaning of the quotation remains clear when removed from context.” (p. 250). So, each of the codes was concise, easy to understand, and communicated enough detail.

References

Laureate Education (Producer). (2016). Introduction to coding [Video file]. Baltimore, MD: Author.

Saldaña, J. (2016). The coding manual for qualitative researchers (3rd ed.). Thousand Oaks, CA: 

Sage Publications.

Ravitch, S. M., & Carl, N. M. (2016). Qualitative research: Bridging the conceptual, theoretical, 

and methodological. Thousand Oaks, CA: Sage Publications.

 KNOW SPSS AND BE ON TIME!! 

DUE 12/24/17 10 P.M EST 

WHEN GOOD DATA GOES BAD: DATA ATTACH TO USE

Collecting valid data is an art unto itself. Ensuring that your results are valid and unbiased is a skill beyond the scope of this module. However, even quality data can be corrupted by human error and inattention to detail. Some of the most common data errors include:

· Data that are not read properly

· Dates are interpreted improperly

· Hidden characters that result in improper fields (such as a tab character in a field that looks blank but causes SPSS to classify the field as a "string")

· SPSS auto-assigning incorrect levels of measurement

· Data that are out of range

· Impossible categories

· Calculating a nonsensical mean (if 1 = blonde, 2 = brunette, 3 = red hair, 4 = black hair, etc., then a mean hair color of 3.1 provides no useful information)

· Lower- and upper-case letters used interchangeably (such as "f" and "F" for sex)

· Inconsistency of measurement and coding

ASSIGNMENT: Post a list of the errors you found in the data set, as well as a brief explanation of why they are errors.Explain how to identify and fix data errors and, , attempt to identify all 63 errors in the data set. Be sure to understand why the errors in the SPSS file are problematic and how the steps you took to fix those errors. The Application in this module requires you to fix an SPSS data set.

Import the “Module 3 Discussion Data Set” listed  into SPSS. Look for problems in the data and be prepared to explain why they are problems. Addressing all of the errors. Post all of your work, comments, errors and how you fixed the errors to this discussion board. Note that the data set contains a total of 63 errors. 

  

DUE 12/23/17 8 P.M EST KNOW SPSS

WHEN GOOD DATA GOES BAD: DATA ATTACH TO USE

Collecting valid data is an art unto itself. Ensuring that your results are valid and unbiased is a skill beyond the scope of this module. However, even quality data can be corrupted by human error and inattention to detail. Some of the most common data errors include:

· Data that are not read properly

· Dates are interpreted improperly

· Hidden characters that result in improper fields (such as a tab character in a field that looks blank but causes SPSS to classify the field as a "string")

· SPSS auto-assigning incorrect levels of measurement

· Data that are out of range

· Impossible categories

· Calculating a nonsensical mean (if 1 = blonde, 2 = brunette, 3 = red hair, 4 = black hair, etc., then a mean hair color of 3.1 provides no useful information)

· Lower- and upper-case letters used interchangeably (such as "f" and "F" for sex)

· Inconsistency of measurement and coding

ASSIGNMENT: Post a list of the errors you found in the data set, as well as a brief explanation of why they are errors.Explain how to identify and fix data errors and, , attempt to identify all 63 errors in the data set. Be sure to understand why the errors in the SPSS file are problematic and how the steps you took to fix those errors. The Application in this module requires you to fix an SPSS data set.

Import the “Module 3 Discussion Data Set” listed  into SPSS. Look for problems in the data and be prepared to explain why they are problems. Addressing all of the errors. Post all of your work, comments, errors and how you fixed the errors to this discussion board. Note that the data set contains a total of 63 errors. 

   

 

Purpose of Assignment 

The purpose of this assignment is to provide students the opportunity to demonstrate mastery of their ability to apply statistical concepts to business situations to inform data-driven decision-making. The project is a 3-week project, with part 1 in Week 3, part 2 in Week 4, and part 3 in Week 5. In Week 3, students identify the organization, problem, research variable, methods for collecting data, and show mastery of validity and reliability as applied to data-collection methods.

Resources: Week 3 Videos; Week 3 Readings; Statistic Lab 

Tutorial help on Excel® and Word functions can be found on the Microsoft® Office website. There are also additional tutorials via the web offering support for Office products. 

Assignment Steps 

Identify a business problem or opportunity at a company where you work or with which you are familiar. This will be a business problem you use for the individual assignments in Weeks 3-5. It should be a problem/opportunity for which gathering and analyzing some type of data would help you understand the problem/opportunity better. 

Identify a research variable within the problem/opportunity that could be measured with some type of data collection.

Consider methods for collecting a suitable sample of either qualitative or quantitative data for the variable.

Consider how you will know if the data collection method would be valid and reliable. 

Develop a 350-word analysis that includes the following:

• Name and description of the selected company.
• Describe the problem at that company.
• Identify one research variable from that problem.
• Describe the method(s) you would use for collecting a suitable sample of either qualitative or quantitative data for the variable (Note: do not actually collect any data).
• Analyze how you will know if the data collection method would generate valid and reliable data (Note: do not actually collect any data). 

Assignment 1. Linear Programming Case Study (Worth 110 pts)

From your real-world of your business* formulate a linear programming project for this assignment according to the following specifications.

It will be a problem with at least three (3) constraints and at least two (2) decision variables. The problem will be bounded and feasible. It will also have a single optimum solution (in other words, it won’t have alternate optimal solutions). The problem will also include a component that involves sensitivity analysis and the use of the shadow price.

You will be turning in two (2) deliverables, a short writeup of the project and the spreadsheet showing your work.

Writeup.

Your writeup should introduce your solution to the project by describing the problem. Correctly identify what type of problem this is. For example, you should note if the problem is a maximization or minimization problem, as well as identify the resources that constrain the solution. Identify each variable and explain the criteria involved in setting up the model. This should be encapsulated in one (1) or two (2) succinct paragraphs.

After the introductory paragraph, write out the L.P. model for the problem. Include the objective function and all constraints, including any non-negativity constraints. Then, you should present the optimal solution, based on your work in Excel. Explain what the results mean.

Finally, write a paragraph addressing the part of the problem pertaining to sensitivity analysis and shadow price.

Excel.

As previously noted, please set up your problem in Excel and find the solution using Solver. Clearly label the cells in your spreadsheet. You will turn in the entire spreadsheet, showing the setup of the model, and the results.