BUSI820_AssignmentA1

Table of Contents 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? ..................................................................................................................................... 4 A1. 2.2. Why Should You Label the Values of Nominal Variables? .............................................. 4 A1. 2.3. Why Would You Print a Codebook or Dictionary ............................................................ 5 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? ........................................................................................ 5 A1. 2.5. Why Would You Use the Mean Function to Create a Variable, As We Did For The Pleasure Scale? ................................................................................................................................ 5 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? .......................................................................................................... 6 A1. 2.6. (a) Why is It Important to Check Your Raw (Questionnaire) Data Before and After Entering Them into The Data Editor? ................................................................................ 6 A1. 2.6. (b) What Are Ways to Check the Data Before Entering Them? After Entering Them? .......................................................................................................................................... 6 SPSS Problems Chapter 2 ................................................................................................................ 7 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 ...................................................................................... 7 1

BUSI 820 - A1 QUANTITATIVE ANALYSIS 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? ..................... 8 References ...................................................................................................................................... 11 2 8/26/2023

BUSI 820 - A1 QUANTITATIVE ANALYSIS nominal variables. Second, for variables with three or more categories, a critical decision point arises. It's important to determine whether these categories exhibit an inherent order, i.e., if they range from low to high in a meaningful way. If the categories are merely distinct labels without any specific order, labeling the variable as nominal is appropriate (Morgan et al., 2020). A1. 2.3. Why Would You Print a Codebook or Dictionary? Printing a codebook or dictionary is essential as it serves as a comprehensive reference for understanding and managing data. It provides a clear description of variable meanings, aiding in error identification by allowing cross-referencing of data entries. The codebook clarifies variable categories, highlights special codes for missing values, and indicates the measurement level of each variable (Morgan et al., 2020). By creating a tangible resource, researchers ensure efficient data management, validation, and analysis, ultimately facilitating accurate interpretation and utilization of the dataset's information. 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? If words or letters are used instead of numbers, recording is necessary. There are several reasons to recode a variable. It's feasible that errors existed in the initial data coding that require correction. Researchers can conduct a frequency distribution analysis or employ descriptive statistics on a variable to identify any issues that need to be resolved through recoding. A1. 2.5. Why Would You Use the Mean Function to Create a Variable, As We Did For The Pleasure Scale? This approach proves advantageous when participants have responded to the majority of items within a construct or scale encompassing multiple questions. Utilizing SPSS's Mean function enables the calculation of an average score for this variable across all participants, 4 8/26/2023

BUSI 820 - A1 QUANTITATIVE ANALYSIS accommodating those with missing data by utilizing their available item responses for computation (Morgan et al., 2020). However, caution is advised when applying the Mean function for scales with multiple items. This caution becomes crucial if some participants have only answered a limited number of items. For instance, if a construct like math anxiety involves responses to seven items, but certain respondents have only addressed two or three of them, utilizing the Mean function could introduce bias to the results (Morgan et al., 2020). 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? A1. 2.6. (a) Why is It Important to Check Your Raw (Questionnaire) Data Before and After Entering Them into The Data Editor? When inputting information into the data editor, it's of utmost importance to meticulously verify the questionnaire responses both prior to and following entry. This caution is due to the potential for errors during data input, and this verification process offers a chance to rectify any inaccuracies prior to incorporation into the data editor. Furthermore, this verification process presents an opportunity to address inadvertent omissions or incorrect entries, enhancing the completeness and accuracy of the dataset. A1. 2.6. (b) What Are Ways to Check the Data Before Entering Them? After Entering Them? The process of checking data ensures the availability of concise and unambiguous information for entry, subsequently contributing to a more precise dataset. Within the Data Editor, data can be viewed in a spreadsheet-like format, offering diverse functions like the 5 8/26/2023

BUSI 820 - A1 QUANTITATIVE ANALYSIS   N Minimu m Maximu m Mean student height in inches 50 60 75 67.3 same sex parent's height 50 58 76 66.78 sex at birth 50 1 2 1.48 marital status 49 1 3 1.82 age group 50 1 3 1.96 does subject have children 50 0 1 0.52 amount of tv watched per week 50 4 25 11.98 television shows-sitcoms 50 0 1 0.64 television shows-movies 50 0 1 0.36 television shows-sports 50 0 1 0.52 television shows-news shows 50 0 1 0.46 hours of study per week 50 2 38 15.62 student's current gpa 50 2.4 4 3.172 positive evaluation, institution 50 2 5 3.38 positive evaluation, major 49 1 5 3.27 positive evaluation, facilities 50 1 5 2.76 positive eval, social life 50 1 5 3.1 hours per week spent working 49 0 50 26.12 Valid N (listwise) 47       7 8/26/2023

BUSI 820 - A1 QUANTITATIVE ANALYSIS 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? According to Table 1, the mean height of the students is 67.3 inches. The average height of the same-sex parent is 66.78 inches. According to Table 2 , the percentage of students who are males is 52%. According to Table 3, the percentage of students who have children is 52%. Table 2 Sex at birth     Value Count Percent Standard Attributes Position 3 Label sex at birth Type Numeric Format F8 Measuremen t Nominal Role Input Valid Values 1 males 26 52.00 %   2 females 24 48.00 % 8 8/26/2023

BUSI 820 - A1 QUANTITATIVE ANALYSIS References Morgan, G. A.; Barrett, K. C.; Leech, N. L.; Gloeckner, G. W. (2020) IBM SPSS for introductory statistics: Use and interpretation, sixth edition . Taylor and Francis. Kindle Edition. 10 8/26/2023