Follow the directions in the attached Excel spreadsheet and Word document. There are 4 tabs at the bottom in the Excel file. The first tab is coded data (a d
Follow the directions in the attached Excel spreadsheet and Word document.
There are 4 tabs at the bottom in the Excel file. The first tab is coded data (a data entry and codebook) for a set of data that you will be analyzing. The following tabs include an example of each type of test you will be conducting – chi-square, correlation, t-test. After the example is either one or two practice problems. Make sure you follow the directions in the practice section of the three types of answers I want to see. In addition to the directions provided, you can also review the videos at the top of each tab.
https://www.youtube.com/watch?v=ODxEoDyF6RILinks to an external site.
The link is explaining how to analyze data by using Chi-Square. Go to Youtube. You will be able to find out many videos that are very helpful.
It is imperative that you understand the data analysis section. You will be conducting data analysis on your own data, this assignment is designed to help prepare you for this.
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 | ||||||||
| Community | ||||||||
| Lame-O | ||||||||
| Outdoor | ||||||||
| Rec Therapy | ||||||||
| Travel/Tourism | ||||||||
| Total | ||||||||
| Expected Scores | Men | Women | ||||||
| Step 2: Calculate Expected scores- if there were no relationship between gender and track | Expected-Campus REC If there were no difference between gender and Track we would expect about ___% of men in this track this is calculated by total # of men x % of everyone in this track | Campus REC: Same thing for women: if there was no relationship; we would expect ___% of women to be in this track | ||||||
| Step 3: Search for the Chi Test function | Expected-Community: If there were no difference between gender and track we would expect about ___% of men in this track this is calculated by total # of men x % of everyone in this track | Community: Same thing for women: if there was no relationship; we would expect ___% of women to be in this track | ||||||
| Step 4: Select Observed scores for Array 1 | Expected-Lame-O If there were no difference between gender and track we would expect about ___% of men in this track this is calculated by total # of men x % of population in this track | Lame-O Same thing for women: if there was no relationship; we would expect ___% of women to be in this track | ||||||
| Step 5: Select Expected scores for array 2 | Expected-Outdoor If there were no difference between gender and track we would expect about ___% of men in this track this is calculated by total # of men x % of population in this track | Outdoor: Same thing for women: if there was no relationship; we would expect ___% of women to be in this track | ||||||
| Step 6: Make Determination if there is any significant difference based on p value. | Expected-REC Therapy If there were no difference between gender and track we would expect about ___% of men in this track this is calculated by total # of men x % of population in this track | REC Therapy: Same thing for women: if there was no relationship; we would expect ___% of women to be in this track | ||||||
| Expected-travel If there were no difference between gender and track we would expect about ___% of men in this track this is calculated by total # of men x % of population in this track | Travel: Same thing for women: if there was no relationship; we would expect ___% of women to be in this track | |||||||
| Answer from Chi Square Test | ||||||||
| Is there a relationship between gender and track? |
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 | |||||||||
| How to report: | *Remember that the Pearson Correlation function is not based on .05; It is based on the strength of a relationship from 0 to 1; or from -1 to 0. So the above example reports r =.14; this would not represent a stung relationship (i.e. .14 is not close at all to 1.0) | 2 | 2 | 50</td
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