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Chapter 13

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Social Science
SSCI 2900U
Olga Marques

Research Methods- Chapter 13: Quantitative Data Analysis  The common error arises due to quantitative data analysis looks like a distinct phase that occurs after the data have been collected.  U should be fully aware of what techniques u will apply at a fairly early stage for example when u r designing ur questionnaire, observations schedule, coding frame, or whatever. The two main reasons for this are as follows: 1) U can’t apply just any tech. to any variable. Tech. have to b appropriately match to the types of variables that u have created thru ur research. This means that u must b fully conversant with the ways in which diff types of variables are classified. 2) The size and nature of ur sample are likely to impose the limitations on the kinds of tech u can use.  Simple Random sample-  Postal Questionnaire-  Missing Data- An imp issue is how to deal with issue wen stuff are or info is missing. Missing Data arises when respondents fail to reply to a question- either by accident or cuz they do not want to answer the questions.  A type of Variables- one of the things that might strike u wen u look at the questions is that the kinds of info that u receives varies by questions. The considerations lead to a classification of the different types of variables that r generated in the courses of research. The four main types- 1) interval/ratio variables- These r variables where the distances btw the categories r identical across the range of categories. The highest level of measurements and a very wide range of tech of analysis can b applied to interval ratio variables. There is in fact a distinction btw interval and ratio variables, in the the latter r interval variables with a fixed zero point. However, most ratios variables exhibit this quality in social research they r not being distinguished here. 2) Ordinal Variables- These r variables whose categories can b ranked ordered, but the distances btw the categories r not equal across the range. 3) Nominal Variables- these variables also known as categorical variables, comprise categories that cant b rank ordered. 4) Dichotomous Variables- These variables contain data that have only two categories. Their position in relation to other types is slightly ambiguous as they have only one interval. These therefore, can be considered as having attributes of the other three types of variables. They look as though they r nominal variables, but cuz they have only one interval they r sometimes treated as ordinal variables. However it is probably safest to treat them for most purposes as if they were ordinary nominal variables.  Multiple – indicators (multiple items) measures of concepts like Likert scales strictly speaking produce ordinal variables. Many writers argue that they can b treated as though they produce interval/ratio variables, cuz of the relatively large number of categories they generate.  Univariate analysis- refers to the analysis of one variable at a time. 1) Frequency Tables- a frequency table provides a number of ppl and the % belonging to each of the categories for the variables in question. It can be used in relation to all for the different types of variables. If an interval/ratio variable is to b presented in a frequency table format it is invariably the case that the categories will need to b grouped. 2) Diagrams- r among the most frequently used methods of displaying quantitative data. Their chief adv. Is that they r relatively easy to interpret and understand. If u r working with nominal or ordinal variables. The bar chart or pie chart is two of the easiest methods to use. Each bar represents the number of ppl falling in each category. Another way of displaying the same data is thru a pie chart. This also shows the relative size of each slice relative size of the diffe
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