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These are the specific points that you need to address in order to make sure that all assumptions have been met: All of this may sound quite complex. But in reality it is not: Once you conclude that your assumptions have been met, you write something like: Since none of the VIF values were below 0.
Durbin-Watson statistics fell within an expected range, thus indicating that the assumption of no autocorrelation of residuals has been met as well. Finally, the scatterplot of standardised residual on standardised predicted value Writing dissertation results section not funnel out or curve, and thus the assumptions of linearity and homoscedasticity have been met as well.
If your assumptions have not been met, you need to dig a bit deeper and understand what this means. A good idea would be to read the chapter on regression and especially the part about assumptions written by Andy Field.
You can access his book here. This will help you understand all you need to know about the assumptions of a regression analysis, how to test them, and what to do if they have not been met. You have entered height and weight as predictors in the model and self-esteem as a dependent variable.
First, you need to report whether the model reached significance in predicting self-esteem scores. Look at the results of an ANOVA analysis in your output and note the F value, degrees of freedom for the model and for residuals, and significance level.
You need to multiply this value by to get a percentage. Thus, if your R2 value is. Model summary for regression: This value represents the change in the outcome associated with a unit change in the predictor. You can report all these results in the following way: The model explained For every increase in weight by 1 kg, self-esteem decreased by For every increase in height by 1 cm, self-esteem increased by.
Reporting the results of a chi-square analysis As we have seen, correlation and regression are done when all your variables are continuous. Chi-square analysis, which is what we will describe here, is done when all your variables are categorical.
For instance, you would do a chi-square analysis when you want to see whether gender categorical independent variable with two levels: Then you need to report the results of a chi-square test, by noting the Pearson chi-square value, degrees of freedom, and significance value.
You can see all these in your output. You report these values by indicating the actual value and the associated significance level. The closer the value is to 1, the higher the strength of the association.
You can report the results of the chi-square analysis in the following way: This test assesses whether there are significant differences between two groups of participants, where your independent variable is categorical e.
Thus, in our example, you are assessing whether females versus males showed higher determination to read a romantic novel. Now you need to report the obtained t value, degrees of freedom, and significance level — all of which you can see in your results output.
You can say something like: Reporting the results of one-way ANOVA You use one-way ANOVA when you are comparing more than two means — or more specifically, when you have more than two conditions of a categorical independent variable and one continuous dependent variable.
In the t-test example, you had two conditions of a categorical independent variable, which corresponded to whether a participant was male or female. You would have three conditions of an independent variable when assessing whether relationship status independent variable with three levels: Here, you would report the results in a similar manner to that of a t-test.
You first report the means and standard deviations on the determination to read the book for all three groups of participants, by saying who had the highest and lowest mean. Then you report the results of the ANOVA test by reporting the F value, degrees of freedom for within-subjects and between-subjects comparisonsand the significance value.This may be one of the shortest sections of your thesis or dissertation, but it is worthwhile taking great care to write it well.
Essentially, the Abstract is a succinct summary of the research. It should be able to stand alone in representing why and how you did what you did, and what the results . How to write up the results section of your dissertation, broken down into both quantitative and qualitative results so you can focus on what applies.
The results section of an APA format paper summarizes the data that was collected and the statistical analyses that were performed.
The goal of this section is to report the results without any type of subjective interpretation. Organizing your data. Making your Results section easy to read is the most important part.
There is a lot of information that needs to be crammed into a relatively small space, with the help of a . The Results section should be a concise presentation of your research findings that gives only the data and your statistical analysis. It should not include any interpretation of the data - basically, it should be as dry as possible, with no mention of what the results mean or how they were obtained.
Tips for Writing a Results Section Perhaps the best way to use the results section is to show the most relevant information in the graphs, figures and tables.
The text, conversely, is used to direct the reader to those, also clarifying any unclear points.