The results should set you thinking about why?. That is the whole purpose of research. Having decided upon the wording of the hypothesis, the researcher should consider whether there are any other factors that may influence the study. Considering our example of rock type and farm dependence on cereal crops, the following additional factors might be considered.
This is not a full list, but a group of suggested factors that could influence the results of the study. If they do, then the farmer is no longer growing crops on the pure underlying rock type. The researcher should mention these possible problems in their project, and explain how they could have influenced the results obtained.
In some cases the researcher may decide to take steps to reduce outside factors influencing the result, depending upon the hypothesis being tested. Before starting to gather data be sure you know exactly what you need to record. Decide upon a way in which you will write down your results and make sure that you do write them down immediately.
Farms which derive 75 percent or more of their income from cereals might be regarded as dependant on cereal production; those which derive 74 percent or less of their income in this way may be considered as not being dependant on cereal production. When using chi square the data is presented as a table. It is a good idea to prepare an empty table before you start to do any calculating. That way, you can enter your calculations straight into the table as you do them. You thus save time and avoid making mistakes.
This is the number of cereal dependant farms that were found located on each of the rock types. The same is done with the number of cereal dependant farms found on each of the other rock types.
Remember that our hypothesis states that rock type has no effect upon the distribution of farms that draw their main income from growing cereal crops? Well, if that is true, then it would seem reasonable to assume that we should find a roughly even distribution of such farms regardless of the underlying rock type.
To find our expected frequencies we need to find the number of these farms we would expect to find in each area if they were distributed evenly. Just as it seems, the expected frequency is subtracted from the observed one. This may produce a negative number. Cell Structure 3. Membrane Structure 4. Membrane Transport 5. Origin of Cells 6. Cell Division 2: Molecular Biology 1.
Metabolic Molecules 2. Water 3. Protein 5. Enzymes 6. Cell Respiration 9. Photosynthesis 3: Genetics 1. Genes 2. Chromosomes 3. Meiosis 4. Inheritance 5. Genetic Modification 4: Ecology 1. Energy Flow 3. Carbon Cycling 4. Climate Change 5: Evolution 1. Evolution Evidence 2. Natural Selection 3. Classification 4. Cladistics 6: Human Physiology 1. Digestion 2.
Select Smoking as the row variable, and Gender as the column variable. Click Statistics. Check Chi-square, then click Continue. Optional Check the box for Display clustered bar charts. Click OK. What is a high chi square value? A very small chi square test statistic means that your observed data fits your expected data extremely well. In other words, there is a relationship.
A very large chi square test statistic means that the data does not fit very well. How do you do a chi square test in SPSS? Drag and drop at least one variable into the Row s box, and at least one into the Column s box. Click on Statistics, and select Chi-square. Press Continue, and then OK to do the chi square test. How do you find the degrees of freedom for a chi square test? If the observed chi-square test statistic is greater than the critical value, the null hypothesis can be rejected.
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