Using the Excel Data File that you worked on for Last Week's assignment, now you will be engaging in creating visualizations to present the data.
Here is the file in case you do not have it: Data Homework Fall, 2021 (7) (1).xlsxDownload Data Homework Fall, 2021 (7) (1).xlsx
Please look at the Data Coded tab (first tab) and use the data that's been entered.
Your task is to create at least 5 visuals using both table and figure:
1. Create at least 1 table
2. Create at least 3 figures (line graph, bar graph, histogram, pie chart, and infographics).
It is difficult to develop inferential statistics results into visuals, so I recommend you use the basic descriptive statistics to create visuals.
You can create visuals on Excel or Word at your convenience.
Data Coded(Form Responses)
| Data Entry | Code Book | |||||||||||
| Why did you become a recreation major? | When did you decide to be a rec major? | How many of your 1000 fieldwork hours have you completed? | What track are you taking? | How excited are you about being a Recreation Major? | How long have you been enrolled at CSULB? | Are you a Male or a Female? | Do you currently work in recreation? | Question # | Variable | Variable Label | Variable Label | |
| 5 | 2 | 1000 | 2 | 5 | 1 | 1 | 1 | 1 | Why Rec Major? | Advisor | 1 | |
| 4 | 2 | 0 | 1 | 5 | 1 | 1 | 2 | Class | 2 | |||
| 2 | 4 | 800 | 3 | 4 | 2 | 1 | 1 | Friend | 3 | |||
| 3 | 4 | 800 | 3 | 4 | 2 | 1 | 1 | Instructor | 4 | |||
| 3 | 4 | 1000 | 3 | 5 | 2 | 2 | 1 | Work Experience | 5 | |||
| 5 | 4 | 0 | 6 | 3 | 2 | 2 | 1 | |||||
| 1 | 1 | 0 | 3 | 4 | 2 | 1 | 2 | 2 | When Rec Major? | Advisor | 1 | |
| 5 | 2 | 1000 | 3 | 5 | 2 | 1 | 2 | Transferred | 2 | |||
| 3 | 2 | 100 | 6 | 4 | 2 | 1 | 2 | Birth | 3 | |||
| 5 | 2 | 1000 | 3 | 4 | 2 | 2 | 2 | No Success elsewhere | 4 | |||
| 2 | 2 | 50 | 5 | 3 | 2 | 2 | 2 | |||||
| 3 | 2 | 0 | 3 | 5 | 3 | 1 | 1 | 4 | What track? | Campus Rec | 1 | |
| 5 | 1 | 700 | 4 | 4 | 3 | 1 | 1 | Community | 2 | |||
| 1 | 1 | 860 | 1 | 3 | 3 | 2 | 1 | Lame-O | 3 | |||
| 1 | 4 | 200 | 5 | 5 | 3 | 1 | 2 | Outdoor | 4 | |||
| 1 | 2 | 1000 | 6 | 3 | 3 | 1 | 2 | Rec Therapy | 5 | |||
| 3 | 1 | 100 | 6 | 4 | 3 | 1 | 2 | Travel/Tourism | 6 | |||
| 5 | 4 | 700 | 6 | 5 | 3 | 1 | 2 | |||||
| 5 | 4 | 1152 | 2 | 5 | 4 | 1 | 1 | 7 | Sex | Female | 1 | |
| 5 | 1 | 1000 | 3 | 5 | 4 | 1 | 1 | Male | 2 | |||
| 1 | 4 | 900 | 5 | 4 | 4 | 1 | 1 | |||||
| 5 | 3 | 1000 | 2 | 5 | 4 | 2 | 1 | 8 | Work in Rec? | Yes | 1 | |
| 1 | 4 | 0 | 3 | 3 | 4 | 2 | 1 | No | 2 | |||
| 2 | 2 | 0 | 5 | 5 | 4 | 1 | 2 | |||||
| 5 | 4 | 1300 | 6 | 4 | 4 | 2 | 2 | |||||
| 1 | 4 | 800 | 5 | 4 | 5 | 1 | 1 | |||||
| 4 | 4 | 1000 | 5 | 5 | 5 | 1 | 1 | |||||
| 5 | 3 | 1000 | 4 | 5 | 5 | 1 | 2 | |||||
| 1 | 4 | 500 | 5 | 5 | 5 | 1 | 2 | |||||
| 4 | 4 | 200 | 5 | 5 | 5 | 1 | 2 | |||||
| 1 | 1 | 300 | 4 | 4 | 5 | 2 | 2 | |||||
| 2 | 1 | 100 | 6 | 4 | 6 | 1 | 1 | |||||
Chi Square
| Difference between means (two or more nominal variables) – Chi Square Test | ||||||||
| Is there a relationship between Gender and whether someone works in REC or not? | Test Statistic used = Chi Square | Because we have two NOMINAL level variables | Can also use ORDINAL level variables | YouTube Link to remind you how to do this: | http://bit.ly/ChiSquareExcel | |||
| Example | ||||||||
| Frequency Table of Observed Scores | ||||||||
| Men | Women | Total | Percent | |||||
| Work in REC | 5 | 11 | 16 | 50.00% | ||||
| Not working in REC | 4 | 12 | 16 | 50.00% | ||||
| Total | 9 | 23 | 32 | |||||
| Expected-Work If there were no difference between gender and working in rec, we would expect about 50% of men be working in REC this is calculated by total # of men x % of population work in REC = 9 x 50% | 4.50 | 11.50 | Expected- Same thing for women: if there was no relationship; we would expect 50 of women to work in rec = 23 x 50.0% | Steps to calculate Chi-square using Excel: 1. Code data/survey results 2. Make a frequency distribution table of observed scores 3. Calculate expected scores if there were no relationship for each variable 4. Search for the Chi Test function 5. Select observed (actual) range of scores. 6. Select expected range of scores | ||||
| Expected- No Work- If there were no relationship between gender not working in rec; we would expect about 50% of men to transfer; this is calculated by total # of men x % of population that does not work in rec | 4.50 | 11.50 | Expected-If there was no relationship we would expect about 51% of women to not be working in rec = 23 x 50% | Answer | 0.69 | p > .05 | No relationship between gender and whether someone works in recreation | |
| Practice | ||||||||
| Is there a relationship between Gender and which track people report they are in? | Test Statistic used = Chi Square | Because we have one NOMINAL level variable | And 5 Nominal/Categorical variables | |||||
| Step 1: Create a frequency table of observed scores for gender and track | ||||||||
| Observed Scores | Men | Women | Total | Percent | ||||
| Campus Rec | 1 | 2 | 3 | 9% | ||||
| Community | 1 | 1 | 2 | 6% | ||||
| Lame-O | 0 | 1 | 1 | 3% | ||||
| Outdoor | 2 | 2 | 4 | 13% | ||||
| Rec Therapy | 1 | 2 | 3 | 9% | ||||
| Travel/Tourism | 4 | 15 | 19 | 59% | ||||
| Total | 9 | 23 | 32 | 100% | ||||
| Expected Scores | Men | Women | ||||||
| Step 2: Calculate Expected scores- if there were no relationship between gender and track | 9 × 3/32 = 0.84 → If there were no difference between gender and track, we’d expect 9.4% of men in this track | 0.84 | 2.16 | 23 × 3/32 = 2.16 → Same for women, we’d expect 9.4% of women in this track | ||||
| Step 3: Search for the Chi Test function | 9 × 2/32 = 0.56 → If no difference existed, we’d expect 6.3% of men in this track | 0.56 | 1.44 | 23 × 2/32 = 1.44 → Same for women, 6.3% would be in this track | ||||
| Step 4: Select Observed scores for Array 1 | 9 × 1/32 = 0.28 → If no difference existed, 3.1% of men would be in this track | 0.28 | 0.72 | 23 × 1/32 = 0.72 → Same for women, we’d expect 3.1% in this track | ||||
| Step 5: Select Expected scores for array 2 | 9 × 4/32 = 1.13 → If no difference, we’d expect 12.5% of men in this track | 1.13 | 2.88 | 23 × 4/32 = 2.88 → Same for women, we’d expect 12.5% in this track | ||||
| Step 6: Make Determination if there is any significant difference based on p value. | 9 × 3/32 = 0.84 → If no difference, we’d expect 9.4% of men in this track | 0.84 | 2.16 | 23 × 3/32 = 2.16 → Same for women, we’d expect 9.4% in this track | ||||
| 9 × 19/32 = 5.34 → If no difference, 59.4% of men would be in this track | 5.34 | 13.66 | 23 × 19/32 = 13.66 → Same for women, 59.4% would be in this track | |||||
| Answer from Chi Square Test | 0.13 | |||||||
| Is there a relationship between gender and track? | p = 0.89 (no relationship). | |||||||
| No, there is no statistically significant relationship between gender and the track chosen (p = 0.89). |
Correlation
| Correlations (between two interval/ratio variables) – Pearson Correlation | Data Entry | ||||||||||||||||||
| Is there a relationship between number of years enrolled and excitement? | YouTube Link to remind you how to do this: | http://bit.ly/ExcellPearsonR | Why did you become a recreation major? | When did you decide to be a rec major? | How many of your 1000 fieldwork hours have you completed? | What track are you taking? | How excited are you about being a Recreation Major? | How long have you been enrolled at CSULB? | Are you a Male or a Female? | Do you currently work in recreation? | |||||||||
| 4 | 2 | 0 | 1 | 5 | 1 | 1 | 2 | ||||||||||||
| 5 | 2 | 1000 | 2 | 5 | 1 | 1 | 1 | ||||||||||||
| Example | 1 | 1 | 0 | 3 | 4 | 2 | 1 | 2 | |||||||||||
| 1. Sort the Columns before you start. 2. Search for correl function 3. Select Array 1 as one variable 4. Select Array 2 as the other variable | How to Sort: | Remember to "sort" the columns in order to select the "array" you want- so if you want to measure years be sure to sort the years column. To sort select the entire Data Book (M2:T34) and click the Sort button. The select Sort by (for this example select years), click OK. | 2 | 4 | 800 | 3 | 4 | 2 | 1 | 1 | |||||||||