Applying OB Theory to Problem Analysis
Organization Behavior 3 Project
Project: Applying OB Theory to Problem Analysis
The Week 3 assignment is considered a major assignment that might take more than one week to complete.
Using the same organization you identified in Week 1, assess the root causes of organizational problems using individual OB themes.
Now that you have done an initial assessment and done some background research (i.e., the Week 1 paper), consider the following questions: who do you need to ask, what do you need to ask, and how will you gather the data needed to support or reject your initial assessment about the organizational problems? Because you have studied additional OB concepts since your Week 1 work, be sure to consider all you have studied in the first three weeks as you frame problems.
In preparation for your Week 3 paper, design a data-gathering plan that addresses the who, what, and how questions noted in the previous paragraph. Consult your instructor for guidance as needed. Keep your focus on individual OB themes. Implement as much of your plan as possible and gather some data.
In your paper, address the following:
Summarize the key details about your organization (what its name is, what it does, etc.).
Summarize your initial assessment of organizational problems: what are the symptoms, what is the evidence, and what are the relevant individual OB themes? Include new themes that have emerged in your studies throughout the course.
Justify your data-gathering plan, including details about who, what, and how you will ask. Your data-gathering plan needs to reflect doctoral-level development. Focus on the data needed to confirm whether your proposed, individual OB themes are possible root causes of the organizational difficulties. For example, if you are proposing some type of motivational theme, then you will need to collect specific data on motivation.
Assess the information gleaned from implementing your data-gathering plan. What did you learn? Did your findings support or refute your initial assessment? What do your findings tell you about symptoms of the problem versus the root causes? Use appropriate academic peer-reviewed literature to support your argument. You may also use documentation and interviews from the organization to support your perspective.
Summarize a self-assessment of your framing process. Why do you see the symptoms, problem, evidence, themes, or other patterns in the situation the way that you do?
Synthesize your work to date into a succinct statement of the organization’s issues. At this point in your work, what do you think are the organization’s problems or gaps that need to be addressed?
Complete your analysis in a 6- to 8-page Microsoft Word document, using APA style.
Name your document SU_MGT7100_W3_LastName_FirstInitial.doc.
While life expectancy continues to increase, differences exist between men and women. As a general rule, women outlive men, yet there are a few countries where men survive longer than women.
What are some reasons females outlive men in the United States and most countries of the world?
What are some reasons that men outlive women in countries other than the United States?
What are some of the factors that contribute to these gender differences?
As a first part to this question, describe what social and psychological factors influence you to recognize and interpret your symptoms. When do you personally decide to seek medical attention? What role does illness representation and illness schema play in your decision to seek medical attention?
What role have these factors played during the Covid-19 pandemic? Do you feel Covid-19 has changed any of these? Why or why not?
Create a Jupyter notebook containing a title of “homework 2”, your name, and the course CPSMA 4313. Load any libraries you will use in a code block at the beginning.
Gather the data from a fitbit provided on Kaggle and provided in the github repo for this course. The link is provided here https://raw.githubusercontent.com/nurfnick/Data Viz/main/Activity Dataset V1.csv. I have used ‘quotes’ when discussing a column in the dataset.
(10 points) Store the data as a pandas dataframe. Examine each datatype and comment on the appropriateness of each.
(10 points) Remove the column that repeats the indexes and is ‘unnamed’ as a column.
(10 points) Clean the column names to remove the unit declaration, (%), using regular expressions. The column name should not have any trailing spaces after cleaning. You will only receive partial credit for simply renaming columns without using regular expressions.
(10 points) Convert ‘activity day’ column into a datetime format.
(10 points) Impute ‘total steps’ by replacing the ‘NaN’s with an appropriate number of steps. Convert to appropriate datatype.
(10 points) Convert non-empty ‘avg pace’ into a float that is still representative of the information contained in the column. Recall that there are 60 seconds in one minute so 3:30 is equivalent to 3.5 minutes.
(10 points) Group data by ‘workout type’ and find the mean, median, count and standard deviation of ‘calories’.
(10 points) Create an indicator column that identifies if the activity achieved 30% or more ‘aerobic’ activity.
(10 points) Which day of the week (Monday, Tuesday, etc.) and ‘workout type‘ has the maximum of the ‘max cadence’.