Reference no: EM13845109

Introduction and Background

The Defense Logistics Agency Land Maritime (DLA L&M) is the premier logistics agency supplying spare parts to the United States warfighter.  DLA L&M is located in Columbus, OH, with locations across the world, employing over 26,000 employees, and annual sales of over 5 million dollars.  The agency manages over 2 million items and provides support to the United States Army, Navy, Marines, and Airforce.   The command structure is a joint venture between military and civilian leadership.  One of the most value assets to the success of DLA L&M is the organization's Human Capital.  A great of investment is put into the successful recruitment of highly qualified and talented individuals to embetter the organization (DLA L&M website).  

The investment in human capital DLA L&M makes with each individual who is recruited and selected, which then builds into the ongoing success or failure of the organizational culture.  The DLA Land & Maritime mission, vision, values, and DLA L&M Way create the emphasis placed upon corporate culture and stress the importance of a healthy "climate". 

Hypothesis

The primary ways culture is expressed at DLA Land and Maritime is through the utilization of the Denison Survey Model 360 Degree Feedback Survey (between associates and levels of supervision) and implementation of Social Contracts between management and associates.  The first measure is through a climate survey created by Denison Group and based upon the Denison model(s).  The use of this tool to assess and measure if levels of corporate culture started in 1999, when DLA Command saw a gap in areas of communication between leadership and associates.  The 360 Degree Survey and Social Contracts were implemented to assist leadership in becoming aware of gaps within the organization's structure, and addressing them at the local level (between associates and their chain of command).  The purpose of this project is to hypothesize and assess if the Denison Culture Survey results from 2012 and 2014, to show a downward trend in the culture climate throughout DLA L&M.  The null hypothesis is showing that there has been no or little change in the results over the two year time period. 

Data and Methods

The data set is the comparison to respondents to the survey, which is comprised of questions, over four quadrants, and the population is comprised of 700 Maritime Associates.  The survey is delivered through web based participation, with the population receiving email reminders.    The sample sizes, differ by +/-8 from 2012 to 2014, with the sample size in 2012 being n=357 and in 2014 n=365. 

Much of the testing and theory behind the hypothesis centers on "hypothesis testing", which is basically proving whether the "null" is true.  The null is showing no difference between the mean or variable.  Looking at this there needs to be a correlation or rate of change between the years, to prove the hypothesis correct.  This also needs to be explored through an error variance, to help remove any systematic or unsystematic issues in the data.  Another test which could be utilized is the Analysis of Variance test, which test the differences among the means.   If another variable would be added a Chi-Test could be explored in order to show the relationship between numbered (nominal variables). 

The dependent variable in this test is the categories of the Dension Culture Survey, such asEmpowerment, Core Values, Coordination &Integration, which comprises the four quadrant's cited above.  The independent variable is the tally or percentage total, averaged among the respondents to quantify average(s) of the survey participants. 

Findings

To establish a measure of statistical significance and to show whether or not the null hypothesis is true or not, a one sample T-test method is utilized.  This showed the following

ONE SAMPLE T-Test

Discussion

The testing did not show correctly the variable titles and was not plotted correctly.  However basing the data off of the previous analysis, the null hypothesis would prove to be incorrect based upon the negative correlation in multiple quadrants.  The T-test shows a high number in the confidence interval and the mean difference of the subject(s) measured fall in between the reported sample size response rates.  This show statistical significance.  The difference in between the mean and sample size proves the hypothesis to be correct. 

For this assignment, state a research question, theories, and hypotheses, and then test one of your hypotheses using data from an existing data set. You can use the MCIC, GSS, MIDUS, or ESS dataset, or any other data set you find.

Your paper should have these five sections with these subheadings. Present a paper, not a series of answers to questions.

I. Introduction:

o Write a one-paragraph introduction stating your topic and making a case for its importance.

o The last sentence in the introduction should be your research question.

II. Theories and hypotheses:

o State and explain some causal theories that may answer your research question, and

o State how you will operationalize these into hypotheses using variables from your data set. Don't describe the variables in detail yet - leave that for the next section.

III. Data and methods:

A. First, name and describe the data set in a single paragraph: the sampling method, size of the sample, year, and method of administration (phone, in-person, etc.). You will have to click around the MCIC or GSS codebooks and websites to find this information. Don't write more than one paragraph.

B. Second, describe the variables you will use to operationalize one of your hypotheses.

Include a description of the wording of the questions and answer categories, any recoding you did, and a table with the minimum, maximum, mean, and standard deviation for ordinal and interval variables, and a table with frequencies for nominal variables and dichotomies.

C. Third, describe briefly (a couple sentences will probably do) what methods you will use in SPSS to analyze your data.

You only have to test your variable combination once, with the most appropriate methods.

Use the table from Statistics Guide A if you're not sure what methods to use to test statistical significance (t-test or crosstabs and chi square) and substantive significance (measures of association).

IV. Findings:

Describe the results of your data analysis for whichever methods you used: cross-tabs and chi square, t-tests, correlations, measures of association

Report the numbers and enough information to describe what the numbers mean, but leave your own opinions and comments to the next section.

Report the results for both statistical and substantive significance.

V. Discussion:

Analyze how your results relate to your theory and the one hypothesis.

If you found what you expected, say what you would do next.

If you found unexpected results, speculate on what they might mean, and what you might do in the future to try to explain them.

Attachment:- project one.xlsx