Archived Dataset
Identify one dataset (also referred to as a database) publicly available for research. Employee Productivity can be used.
To operationalize the dataset, gather the following information about the dataset. Dataset information is most likely contained in a document separate from the dataset and may be identified as a database dictionary, codebook, or program record layout. For the assignment response, include the following information using the headings and format outlined here:
ASSIGNMENT OUTLINE START
Dataset Name.
Enter text on this line.
Dataset Source.
Enter text on this line.
Dataset Location.
Enter text on this line. (include link if available)
Dataset Overview.
Include three to five sentences to provide context for the Reader. This may include industry, focus, and original purpose for collecting the data.
Dataset Timeframe.
Enter text on this line.
Four Variables.
For each of the four variables, separately list the following information, using separate headings for each variable. If the information is not available, then indicate ‘not available.’ Follow the template below for each of the variables/constructs.
State Variable Name [include dataset abbreviation if appropriate]
Definition of the Variable. Enter text here.
Source of Data. (This might be self-reported by business, self-reported by survey, observation, etc.)
Scoring of the Variable. Enter text here. For variables, this might be age in years, number of defects per quarter, group membership, etc. For constructs, this might be computation of multiple questions on a survey or multiple variables that make up the construct.
Level of Measurement. Enter text here. Below is a reminder of the measurement levels you learned in Statistics 1. Do not include the measurement level definitions in your assignment response.
Nominal. Nominal data are measured at the discrete level depicting independent categories with no underlying order. Examples of nominal data are sex, race, and organizational department membership.
Ordinal. Ordinal data are measured at the discrete level with separate categories that imply an underlying hierarchy. Examples of nominal data are education level, age groups, or simply categories of high, medium, and low. Some Likert scales may be deemed by the researcher as ordinal data.
Interval. Interval data are measured on a continuous scale with ‘equal-appearing’ intervals and without an absolute zero point. Examples of interval data are time, temperature, credit score, and test scores. Some Likert scales may be deemed by the researcher as interval data.
Ratio. Ratio data are measured on a continuous scale with equal intervals and an absolute zero point. Examples of ratio data are age, income, and defects per lot. A researcher may deem an otherwise ratio data as interval if, for purposes of the research, the measurement level truncates the zero point or otherwise holds a floor or ceiling effect.
Score Range and Interpretation. Enter text here. Include the total possible range of scores for the variable and how to interpret the range. The score range and interpretation might be ‘Six-point Likert scale with ‘1’ meaning “Not Satisfied at All,” and ‘6’ meaning ‘Completely Satisfied;’ or perhaps a four-point ordinal scale with ‘1’ meaning no use, ‘2’ meaning little use, ‘3’ meaning moderate use, and ‘4’ meaning a lot of use. For nominal variables that form a discrete category then identify the coding scheme, for example 1 = male; 2 = female.
Reflection.
Enter text here. Describe advantages, disadvantages, challenges, and benefits that you feel should be considered if you were to use an archived dataset for your dissertation research study.
ASSIGNMENT OUTLINE STOP
Length: Your paper should be at least 5, but may be as long as 10 pages, if the table and/or figures are included. This does not include the title and reference page. You are encouraged to make effective use of tables and/or figures in your presentation.
