 Raw data is generated by the process of collecting information  From 20-question survey of 100 people, for example, 2000 ‘bits’ of information are collected.  Raw data needs to be organized, condensed, and presented to reveal overall patterns, along with any striking exceptions and puzzling deviations

 Coding is a way to manually ‘assemble’ the information from a survey.  For example, for each question, you can assign a number to each individual response (e.g. ‘a’ becomes 1, ‘b’ becomes 2, and so on).  Be sure to include ‘no response’ as one of your codes.  When you tabulate your answers, you will generate a chart (as shown on the following slide)

Respondent No. SexMarital Status Musical Preference Etc

 At this point, it is particularly beneficial if you are proficient with a spreadsheet program.  For smaller surveys (fewer than 100 respondents), you can probably do most of your calculations manually.

 One of the most common kinds of analysis that you will do from a survey is called a univariate analysis.  This looks at the results of one variable (e.g. of ALL of the repondants, how many claimed never to have been married?)  From here, you get a frequency distribution (that is, how frequently is a particular response distributed amongst the sample population?)

 Nominal Level Respondents are identified by their response to a particular question  Ordinal Level Allows the researcher to rank categories  For Example If a survey question asked respondents to rank a number of musical styles in order of their own preference or interest, a NOMINAL ranking would calculate the percentage of respondents who selected a particular option (e.g. 40% listen to Rock Music), whereas an ORDINAL ranking would also calculate how many selected rock music as a first choice.

 The Interval Level Helps the researcher to determine how much of a difference exists between one group and another Allows ranking to be done on a scale with regular intervals Does not necessarily indicate one score as a proportion or ratio of another  For Example Interval ranking can be done with grades in school (e.g. >50, 50-59, 60-69, 70-79, 80-89, 90+) One grade is not indicative of a measurable percentage of another (e.g is a mark of 64 really only 80% as successful as an 80?)

 Ratio Level Only used where true zero can be established True zero does not ‘really’ exist for measurements like temperature, I.Q. or school grades (as anyone can get SOME result) Can be used to determine amounts (e.g. spending or income) in order to establish rank and ratio (this is where you get information about income distribution, for example, and terms like “the One Percent.”

 The results do not necessarily speak for themselves.  You need to ask questions about your data, and test various possibilities  Ask questions about your variables – Check results for each option (e.g. What results are there for men? Women? Both? What conclusions can be drawn)  Ask questions which include a variable AND another result (e.g. What results are there for men who HAVE been married, vs men who have not, and the same for women)  The more narrowly you can focus your consideration of the results, the more likely it is that you may uncover something new and interesting.

 How can you tell if your results are ‘right’? Check your sample group – is it the right size? Is it varied enough? How can you identify responses which were not serious ones?  In what way can your hypothesis’ truth be tested by asking questions about your results? You should know in advance some of the information that you need to find - If your hypothesis is true, then the data need to reflect it.

 Pie charts are good for demonstrating percentages (but the number of ‘slices’ needs to be relatively small for it to be meaningful)  Bar graphs are good for demonstrating amounts which are meant for comparison  Line graphs are good for identifying values which change because of a particular variable (e.g. time or temperature or age)

 Read pages  Answer the questions on page 65.  Yeah!