Today, we are going to talk about the research that we have done in the previous class. We were asked to present what we have done for our research. Then, we are introduced to SPSS in the IT class. We were asked to give code to the demography of the respondent. For example, for the sex of the respondent, 1 represent male and 2 represent female. For the academic background, 1 represent nursing student Diploma 2nd year and 2 represent nursing student Degree 2nd year and 3 represent nursing student Degree 3rd year.
Then we were asked to open the SPSS in the computer. We were asked to explore the SPSS by reading on some of the example in the computer which has key in the data in the SPSS. In the SPSS, there are 2 types of view which are “data view” and “variable view”. To start to use the SPSS, we should start from the ‘variable view”. There are 10 column in the “variable view” which consist of ‘name’, ‘type’, ‘width’, ‘decimals’, ‘label’, ‘values’, ‘missing’, ‘columns’, ‘align’, and ‘measure’. To key each of the research data that you have colleted, you will need to fill in all the details in the variable view before you key in the data. First, you will need to name the question, then select “numeric’ for the variable type” and at the same time I change the decimals into zero before for the data that I collect do not need decimal. Then in the label column, I fill in more details about each of the data question. The values column is the answer of my question in the research which is represent by code also at the same time. In the measure column, there are 3 types of measures that you can choose which are ‘scale’, ‘nominal’ and ‘ordinal’.
Nominal Data
A set of data is said to be nominal if the values / observations belonging to it can be assigned a code in the form of a number where the numbers are simply labels. You can count but not order or measure nominal data. For example, in a data set males could be coded as 0, females as 1; marital status of an individual could be coded as Y if married, N if single.
Ordinal Data
A set of data is said to be ordinal if the values / observations belonging to it can be ranked (put in order) or have a rating scale attached. You can count and order, but not measure, ordinal data. The categories for an ordinal set of data have a natural order, for example, suppose a group of people were asked to taste varieties of biscuit and classify each biscuit on a rating scale of 1 to 5, representing strongly dislike, dislike, neutral, like, strongly like. A rating of 5 indicates more enjoyment than a rating of 4, for example, so such data are ordinal. However, the distinction between neighbouring points on the scale is not necessarily always the same. For instance, the difference in enjoyment expressed by giving a rating of 2 rather than 1 might be much less than the difference in enjoyment expressed by giving a rating of 4 rather than 3.
Then we were asked to open the SPSS in the computer. We were asked to explore the SPSS by reading on some of the example in the computer which has key in the data in the SPSS. In the SPSS, there are 2 types of view which are “data view” and “variable view”. To start to use the SPSS, we should start from the ‘variable view”. There are 10 column in the “variable view” which consist of ‘name’, ‘type’, ‘width’, ‘decimals’, ‘label’, ‘values’, ‘missing’, ‘columns’, ‘align’, and ‘measure’. To key each of the research data that you have colleted, you will need to fill in all the details in the variable view before you key in the data. First, you will need to name the question, then select “numeric’ for the variable type” and at the same time I change the decimals into zero before for the data that I collect do not need decimal. Then in the label column, I fill in more details about each of the data question. The values column is the answer of my question in the research which is represent by code also at the same time. In the measure column, there are 3 types of measures that you can choose which are ‘scale’, ‘nominal’ and ‘ordinal’.
Nominal Data
A set of data is said to be nominal if the values / observations belonging to it can be assigned a code in the form of a number where the numbers are simply labels. You can count but not order or measure nominal data. For example, in a data set males could be coded as 0, females as 1; marital status of an individual could be coded as Y if married, N if single.
Ordinal Data
A set of data is said to be ordinal if the values / observations belonging to it can be ranked (put in order) or have a rating scale attached. You can count and order, but not measure, ordinal data. The categories for an ordinal set of data have a natural order, for example, suppose a group of people were asked to taste varieties of biscuit and classify each biscuit on a rating scale of 1 to 5, representing strongly dislike, dislike, neutral, like, strongly like. A rating of 5 indicates more enjoyment than a rating of 4, for example, so such data are ordinal. However, the distinction between neighbouring points on the scale is not necessarily always the same. For instance, the difference in enjoyment expressed by giving a rating of 2 rather than 1 might be much less than the difference in enjoyment expressed by giving a rating of 4 rather than 3.
For more information, you can visit http://www.stats.gla.ac.uk/steps/glossary/presenting_data.html#nomdat or http://www.datastep.com/SPSSTutorial_1.pdf
Then after setting all the variable view, You can start to key in the data that you collected from the respondent. because the research is a group work so we divede the questionnare into 3 and each of us key in data from 20 questionnare. After 3 of us has key in, we combine the 3 files into one. How? first, open the first part of the file in spss, then, go to select 'Data' at the left above corner of the page, select 'Merge Files', then Select 'Add Cases' and a window will pop out and lastly click ok on the window. Then the files is combined. But, must make sure that the variable view you set must exactly the same with your friends so that you can combined it.
After combined all the file in one single file, you can create a summary by using the SPSS software. You can also calculate the data that you have key in by using the software. You do not need to calculate it manually. For example, you can click on 'analyze' on the left corner of the page in the spss, then, click on 'Descriptive Statistics". There are 5 choices that you can choose on to analyze your data such as ' Frequencies', 'Descriptives', 'Explore', 'Crosstabs' and ' Ratio'. You need to choose the suitable and appropriate types to analyze your data. For example, due to my data mostly is nominal type, so i can't choose 'Descriptives' to analyze my data. Ordinal types data is more suitable to choose on 'Descriptives'.
You can also click on 'Graphs' on the above of the page of spss. Then, you can graph bar , line or pie to analyze your data which you think is suitable for the data. All the graphs and table of frequencies will appear in SPSS viewer which you can save the viewer in '.spo' format or you can copy the graphs or table from the spss viewer to the Microsoft Word. After finish using the data in spss, you can save the data in '.sav' in order to let you open again in the next session.
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