Business Studies
Interpretation Questions
Chapter 2
A1. 2.1. What steps or actions should be taken after you collect data and before you run the analyses aimed at answering your research questions or testing your research hypotheses?
A1. 2.2. Why should you label the values of nominal variables?
A1. 2.3. Why would you print a codebook or dictionary?
A1. 2.4. What do you do if you look at your data file and see words or letters instead of numbers? Why is this important to do?
A1. 2.5. Why would you use the Mean function to create a variable, as we did for the pleasure scale?
A1. 2.6. (a) Why is it important to check your raw (questionnaire) data before and after entering them into the data editor? (b) What are ways to check the data before entering them? After entering them?
SPSS Problems
Chapter 2
Using the CollegeStudentData.file do the following problems. Print your outputs and circle the key parts for discussion.
Compute the N, minimum, maximum, and mean for all the variables in the College StudentData.sav file:
How many students have complete data? Identify any statistics on the output that are not meaningful. Explain.
What is the mean height of the students? What about the average height of the same-sex parent? What percentage of students are males? What percentage have children?
Write the problem number and the problem title as a level one heading (Example ‐ A.1.1: Chapter 2, Problem 2.1, and then provide your response.
Use level two headings with short titles for multi part questions (Example ‐ A1.1.a, Short Title, A1.1.b, Short Title II, etc.)
Use appropriate level headings for key elements of your discussion such as Research Questions, Hypotheses, Descriptive Statistics, Assumptions & Conditions, Interpretation, Results, and others. Your goal is to make your analysis easy to follow and logical.
Ensure that all tables and graphs are legible and include a figure number.
Carefully review your document prior to submission for formatting, flow, and readability. Keep in mind that running the statistical tests is only the first half of the challenge; you must be able to clearly communicate your findings to the read
ANSWER
Interpretation Questions
Chapter 2
A1. 2.1. What steps or actions should be taken after you collect data and before you run the analyses aimed at answering your research questions or testing your research hypotheses?
The first thing is to ensure that there are no data errors or other problems related to data such as unreliable data, missing data and misleading data. There are raw data quality issues that are important to check such as inconsistencies and errors (Zuur, Ieno and Elphick, 2010). Therefore, at this point, the unwanted data points will be removed to ensure only the relevant data remains for analysis. Relatively, it’s important to find relevant data to make it easier to access and find data especially when dealing with large volumes of data. Before running the analysis it’s important to code the data and add values especially through numbers. Coding and adding labels will bring structure of the data thus finding the layout issues which will assist in mapping and manipulating the data more effectively.
A1. 2.2. Why should you label the values of nominal variables?
The variable is nominal since the value of a nominal variable does not represent orders or ranks but groups based on how they are answered. Nominal variable is a type of variable utilized in naming, labelling and categorizing certain attributes that are being measured (Ali and Bhaskar, 2016). Labelling the values of nominal valuables makes it easier to understand the data. It’s strongly suggested that one gives a value to every label to ensure everyone looking into the data and results understand what each value represents.
A1. 2.3. Why would you print a codebook or dictionary?
A codebook is used in describing the structure, contents and layout of the data collected. A well-documented codebooks should contain complete information and self-explanatory for every variable in the data file. Codebook should be printed since they serve as a dictionary data and documentation of data set that contain critical information. The codebooks offers a way to check the data entered for each variable so that they understand the meaning of each variable and its level (Pienta, O’Rourke and Franks, 2011). The codebook will also help anyone accessing the data to ensure that the data was entered is correct. The codebook will describe the layout out of the data file and what the data codes entails.
A1. 2.4. What do you do if you look at your data file and see words or letters instead of numbers? Why is this important to do?
A data file containing words or letters rather than the numbers means that the information is meant to be read, viewed and not executed (Muşlu, Brun, and Meliou, 2015). Mostly, words or letters appear when the data file has an error or missing data that prevents the data from being complete. Therefore, if the data file contains letters or words instead of numbers, it’s important to counter check the original data and ensure that the file was imported correctly.
A1. 2.5. Why would you use the Mean function to create a variable, as we did for the pleasure scale?
The mean function is the average value of the function which gives an idea of the random process. Creating a variable through the mean function is important since the mean accounts for the missing data. The mean is the center of gravity for the data set and offers a generally acceptable response as a basic statistic that covers the whole data set (Parke, 2012). Therefore, the mean function will calculate the mean of the missing data through using the average of the available data.
A1. 2.6. (a) Why is it important to check your raw (questionnaire) data before and after entering them into the data editor? (b) What are ways to check the data before entering them? After entering them?
Before entering the raw data into the data editor, it’s important to check the raw data for cleaning in order to identify and remove responses that do not match the target audience thus eliminating unacceptable questionnaires. It allows for examining the data to ensure that it has been accurately recorded. It’s important to check the raw data after entering them into the data editor since there might be type errors or mistakes. This offers a chance to countercheck and make corrections on the data entered. Relatively, there is a chance of entering the missing value which by mistake might have not been entered properly.
Problems Chapter 2
Compute the N, minimum, maximum, and mean for all the variables in the College StudentData.sav file:
How many students have complete data? Identify any statistics on the output that are not meaningful. Explain.
The number of students that have completed the data are 50. The marital status and gender of student are not meaningful since the means is too low. The variance was not meaningful since it offers a measure of dispersion that takes into account the spread of all data points within the data set. The variance is not meaningful in making prediction of the data in any of the report.
What is the mean height of the students?
63.7 inches
What about the average height of the same-sex parent?
66.78 inches
What percentage of students are males?
74% are male
What percentage have children?
52% have children
SPPS software is an effective statistical tool used for complex statistical data analysis in a user friendly manner (Morgan, Leech, Gloeckner and Barrett, 2013). It allows the users to gain significant insights of the study being conducted. The SPPS software offered significant results on the student data analysis. There were different valuable statistics used in the data analysis process and simply the data analysis process.
References
Ali, Z., & Bhaskar, S. B. (2016). Basic statistical tools in research and data analysis. Indian journal of anaesthesia, 60(9), 662.
Morgan, G., Leech, N., Gloeckner, G., Barrett, K. (2013). IBM SPSS for Introductory Statistics (5th Ed.). New York, NY
Muşlu, K., Brun, Y., & Meliou, A. (2015, July). Preventing data errors with continuous testing. In Proceedings of the 2015 International Symposium on Software Testing and Analysis (pp. 373-384).
Parke, C. S. (2012). Essential first steps to data analysis: Scenario-based examples using SPSS. Sage Publications.
Pienta, A. M., O’Rourke, J. M., & Franks, M. M. (2011). Getting started: Working with secondary data. Secondary data analysis: An introduction for psychologists, 13-25.
Zuur, A. F., Ieno, E. N., & Elphick, C. S. (2010). A protocol for data exploration to avoid common statistical problems. Methods in ecology and evolution, 1(1), 3-14.
