SPSS (The Statistical Package for the Social Sciences) software has been developed by IBM and it is widely used to analyse data and make predictions based on specific collections of data. SPSS is easy to learn and enables teachers as well as students to easily derive results with the help of a few commands. The implications of the results are fairly evident and are statistically valid. Using the software, one can conduct a series of studies quickly and effectively. If you are worried about conducting your data analysis on SPSS, here are a few guidelines and an overview of the process.
Steps
Image titled Analyse Data Using SPSS Step 1
1
Load your excel file with all the data. Once you have collected all the data, keep the excel file ready with all data inserted using the right tabular forms.
Image titled Analyse Data Using SPSS Step 2
2
Import the data into SPSS. You need to import your raw data into SPSS through your excel file. Once you import the data, the SPSS will analyse it.
Image titled Analyse Data Using SPSS Step 3
3
Give specific SPSS commands. Depending on what you want to analyse, you can give desired commands in the SPSS software. Each tool has guidelines on how it should be used and you can feed in all the options to get the most accurate results. Giving commands in SPSS is simple and easy to comprehend, making it an easy task for students to do this by themselves.
Image titled Analyse Data Using SPSS Step 4
4
Retrieve the results. The results from the software are given efficiently and accurately, providing researchers a better idea of appropriate future studies and a direction for moving forward.
Image titled Analyse Data Using SPSS Step 5
5
Analyse the graphs and charts. Understanding the results can be a little difficult. but you can get help from professors and peers with the analysis. You can also consult a professional company which is expert in SPSS.
Image titled Analyse Data Using SPSS Step 6
6
Postulate conclusions based on your analysis. The ultimate objective of the SPSS is to help arrive at conclusions based on specific research. The software helps you to derive conclusions and predict the future easily with minimum statistical deviation.
Parametric or Nonparametric data
Before choosing a statistical test to apply to your data you should address the issue of
whether your data are parametric or not. This is quite a subtle and convoluted decision but
the guide line here should help start you thinking, remember the important rule is not to
make unsupported assumptions about the data, don’t just assume the data are parametric;
you can use academic precedence to share the blame “Bloggs et. al. 2001 used a t-test so I
will” or you might test the data for normality, we’ll try this later, or you might decide that
given a small sample it is sensible to opt for nonparametric methods to avoid making
assumptions.
• Ranks, scores, or categories are generally non-parametric data.
• Measurements that come from a population that is normally distributed can usually
be treated as parametric
If in doubt treat your data as non-parametric especially if you have a relatively small
sample.
Generally speaking, parametric data are assumed to be normally distributed – the normal
distribution (approximated mathematically by the Gaussian distribution) is a data
distribution with more data values near the mean, and gradually less far away,
symmetrically. A lot of biological data fit this pattern closely. To sensibly justify applying
parametric tests the data should be normally distributed.
If we you unsure about the distribution of the data in our target population then it is safest
to assume the data are non–parametric. The cost of this is that the non parametric tests are
generally less sensitive and so you would stand a greater chance of missing a small effect
that does exist.
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital
fundlydigital