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For this lab, you will be processing raw responses to the class census in order to test two research hypotheses: For the purposes of this lab, assume that the class census was distributed as an online survey to a random sample of UO students. You are aiming to generalize your results to the UO student population as a whole. To answer your research questions, you will need to organize and categorize the data produced by Qualtrics in an Excel spreadsheet. You will need: We strongly suggest using a word processing application to type up and save your answers before pasting them into Canvas. (This will avoid losing work if Canvas has issues.) Where open written answers are required, please . Approximate length of answer is specified for each question. if you don’t already have one. Then save both an original copy of the Excel file (eg “GEOG391 Census Sum2018 LabM4” from Canvas) and a separate, on your computer (eg., “Lab M4_Working copy”). It is easy to accidentally delete data or mix up variables in Excel, so you should get in the habit of saving an original copy that you do not edit. . Are the questions in order? What do rows and columns represent? How can you tell which question the responses in each column are answering? What kind of data is in each column? Which columns are ordinal, nominal, etc.? You will only need part of the data to test your hypotheses and answer all the questions in the lab. Delete all the columns (select the entire column by clicking the letter, then “Edit” -> “Delete”) for: Q16_3, Q16_5, Q7, Q10, and Q29. 4. Not all of the respondents that started the census completed it: there are some blank rows in the remaining data. Go ahead and delete those blank lines. (Note: it can be hard to see, but line 44 is missing a response to Q29). How many responses are left? Given the size of the sample (there are 88 students in the class), are there enough non-responses to threaten the validity of the data? In order to get from the data to a test of the hypothesis, you will

have to turn the responses into counts. First, you will set up a cross-tab (See Schutt 2017, 182-185). For , we are testing the relationship between the importance of food to culture and ethnicity. Accordingly, your cross-tab will have one variable (importance of food to culture) tabulated against the other variable (ethnicity). In the data we measured “importance of food to culture” using a self-reported indication of agreement with a five-level ordinal ranking, ranging from “strongly agree” to “strongly disagree.” We measured ethnicity using a self-reported indicator, using categories drawn from the US 2010 Census. Looking at hypothesis 1, the only ethnic distinction that matters to this question is the distinction between “white” and “other”. Therefore, the crosstab should look like this: WhiteOther(totals)Strongly agreeSomewhat agreeNeither agree nor disagreeSomewhat disagreeStrongly disagree(totals) Complete for the first rows of the data. If there is no relationship between ethnicity and the importance of food, then the numbers of respondents of each group who picked each responses should be roughly similar. Here is a complete cross-tab for the whole class: WhiteOther(totals)Strongly agree1323 Somewhat agree149 Neither agree nor disagree63 Somewhat disagree21 Strongly disagree11 (totals) It looks like white-identifying respondents tended to assign a lower rank to the importance of food to their culture. But is that relationship ? To see, run a chi-square test using . Select the appropriate number of rows (5) and columns (2) in the first screen, and then in the second screen enter the data above. Click calculate. Is the relationship statistically significant? (nt: look for the -value.) Complete the cross-tab visualizing the relationship between international student status and agreement that food is the best part of traveling.   Complete this cross-tab for the first rows of class census data. The -value for this crosstab is about ~.22; well short of statistical significance.