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3.3: Surveys

  • Page ID
    308801
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    There are two types of survey questions: Open-ended questions are questions designed to get respondents to answer in their own words (e.g., "What might be the benefits of having a Lacrosse team?" \(\_\_\_\_\) . Closed-ended questions are questions designed to get respondents to choose from a list of responses you provide to them (e.g., "Are you married?" Yes or No.) Likert scale questions are statements which respondents are asked to agree or disagree with. They are the most common types of questions used in surveys (e.g., "How much do you agree that the president is doing a good job of running the country?" Strongly Disagree, Disagree, Neither Agree nor Disagree, Agree, Strongly Agree). Demographic questions are questions which provide the basic categorical information about respondents such as age, sex, race, educational level, marital status, etc.

    Levels of Measurement

    Nominal level data is data with no standard numerical values. This is often referred to as categorical data (e.g., What is your favorite type of pet? \(\_\_\_\_\) Reptile \(\_\_\_\_\) Canine \(\_\_\_\_\) Feline \(\_\_\_\_\) Bird \(\_\_\_\_\) Other). There is no numerical value associated with reptile that makes it more or less valuable than a canine or other type of pet. Other examples include sex, favorite color, or town you grew up in.

    Ordinal level data is categories with an order to them. One category is more of something than another category. For example height measured as short, medium, and tall is ordinal because medium is more height than short and tall is more height than both short and medium.

    Interval level data is categories with an order, but we add standard numerical values with regular intervals. If we measure height in feet and inches we have interval data. A height of 5 feet, 3 inches is 8 inches away from 5 feet, 11 inches. Each of those 8 inches has the same value, the intervals are identical. Five feet, 3 inches is one of the categories, but in this case the categories are numbers. The Fahrenheit temperature scale is an example of an interval scale. The difference between 68 degrees and 72 degree is the exact same four degrees as the difference between 101 degrees and 105 degrees.

    Ratio level data adds a real zero starting point for the numerical values. We can create ratios with ratio level data. With ratio data we can say that someone who has two children has twice as many children as someone having only one child, and someone having four children has twice the children of someone who has just two children, and the person with four children has four times the number of children as the person with only one child. Ratio data is used to compare to other data. For example, the sex ratio is the number of males per 100 females in a society. In 2006, the sex ratio for Alaska, Rhode Island, and the U.S. was Alaska 107; Rhode Island 93.6, and U.S. 97.1. \({ }^3\) We can say that Alaska had more males than females (107 males per 100 females) while Rhode Island had more females than males (93.6 males per 100 females). The U.S. overall has more females than males (97.1 males per 100 females).

    Number of males and females, opinions about a Lacrosse team, marital happiness, height, and sex are variables. Variables vary by respondent (one is male, the next is female, the next is female, etc.). Sex is the variable and male or female are the attributes, or the possible choices. Everyone in your class is human, so humanness is not a variable-it doesn't vary. But almost everything else you can observe is a variable.

    Two types of variables are dependent and independent variables. Dependent variables change in response to the influence of independent variables; they depend upon the independent variables. Independent variables are variables that when manipulated will stimulate a change upon the dependent variables. If I know the independent variable can I predict what the dependent variable will be? If I know that you possess many of the characteristics of happy marriages can I predict your level of happiness? Yes. That doesn't mean that everyone with many of the characteristics will be the happiest, but more often than not, they will be. So possession of characteristics is the independent variable and happiness is the dependent variable. How happy you are depends on how many of the characteristics you possess.

    Is this a causal relationship or merely an association or correlation? A causal relationship is when one variable actually causes the other to occur, such as eating lots of Krispy Kreme donuts causes you to gain weight. That's pretty clear, but in sociology most relationships are not that clear. Do I know for certain that possession of many of the characteristics that are found in happy marriages causes a marriage to be happy? No. What if there is something else that is causing both happiness and possession of characteristics? Maybe it's religion or optimistic personality or something else. If this is true then this is an association or correlation. They go hand in hand, but one does not cause the other.

    Quantitative Analysis

    When basic statistics are performed on data, we call them measures of central tendency (mean, median, and mode). Consider this list of numbers which represents the number of movies that nine students have seen in the last two weeks: \(0,1,1,1,3,4,4,5,8\).

    The mean is the arithmetic score of all the numbers divided by the total number of students (i.e., \(27 \div 9=3\) ). The median is the exact mid-point value in the ordered list of scores (e.g., \(0,1,1, \& 1\) fall below and \(4,4,5, \& 8\) fall above the number 3 thus 3 is the median). The mode is the number which occurs most often (e.g., 1 occurs the most, so the mode is 1 ). The extreme values or outliers are the especially low or high number in the series (e.g., 8). Notice that if you removed the \(9^{\text {th }}\) student's score and averaged only the remaining scores the mean would be 2.375 . Extreme values can increase or decrease the mean. You will cover these basic and more interesting statistics in your statistics class.


    Footnotes

    3. http://factfinder.census.gov/servlet...ad_nbr=R0102&- ds_name=ACS_2005_EST_G00_&-_lang=en&-format=US-30) 5 February, 2009.


    3.3: Surveys is shared under a CC BY 4.0 license and was authored, remixed, and/or curated by LibreTexts.