Here is another example. You might have assessed whether more females than males want to read a specific romantic novel. Here, your independent variable is gender and your dependent variable is the determination to read the book. Since gender has categories male and female , this is a categorical variable. If you have assessed the determination to read the book on a scale from 1 to 10 e. Here, relationship status is your confounding variable. We will return to these examples throughout this blog post.
At this point, it is important to remember that outlining your research in this way helps you to write up your results section in the easiest way possible.
For continuous variables, you are using descriptive statistics and reporting the measures of central tendency mean and measures of variability or spread standard deviation. For categorical variables, you are using frequencies statistics and reporting the number or frequency of participants per category and associated percentages.
Both these statistics require you to make a table, and in both cases you also need to comment upon the statistics. How does all of this look in practice?
Recall the two examples that were outlined above. You need to make a table, as in TABLE 1 below, which identifies means and standard deviations for all these variables. When commenting upon the results, you can say:. Participants were on average Note that, in this example, you are concluding that participants had moderate self-esteem levels if their self-esteem was assessed on a 1 to 10 scale. Since the value of 5 falls within the middle of this range, you are concluding that the mean value of self-esteem is moderate.
If the mean value was higher e. Descriptive statistics for all variables used in research: M SD Height cm You can also outline descriptive statistics for specific groups. Descriptive statistics for the determination to read the book, by gender: Thus, you are not reporting means and standard deviations, but frequencies and percentages.
To put this another way, you are noting how many males versus females wanted to read the book and how many of them were in a relationship, as shown in TABLE 3. You can report these statistics in this way:. Frequencies statistics for all variables used in research: The first of these is correlation, which you use when you want to establish if one or more continuous, independent variables relate to another continuous, dependent variable.
The first step here is to report whether your variables are normally distributed. You do this by looking at a histogram that describes your data. If the histogram has a bell-shaped curve see purple graph below , your data is normally distributed and you need to rely on a Pearson correlation analysis. Here, you need to report the obtained r value correlation coefficient and p value which needs to be lower than.
If you find a correlation, you need to say something like:. Histogram testing the normal distribution of data: Note also that positive correlation occurs when higher levels of one variable correlate with higher levels of another variable. Negative correlation, however, occurs when higher levels of one variable correlate with lower levels of another variable. One final thing to note, which is important for all analyses, is that when your p value is indicated to be.
If your data is skewed rather than normally distributed see red graphs , then you need to rely on a Spearman correlation analysis. Here, you report the results by saying:. You also need to make a table that will summarise your main results.
Correlations between all variables used in research: Height cm Self-esteem Height cm 1. Correlations between all variables used in research, before and after controlling for a covariate: 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 did 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 R 2 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:. 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. 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 comparisons , and the significance value.
There are two things to note here. This test assesses the homogeneity of variance — the assumption being that all comparison groups should have the same variance. If the test is non-significant, the assumption has been met and you are reporting the standard F value. However, if the test is significant, the assumption has been violated and you need to report instead the Welch statistic, associated degrees of freedom, and significance value which you will see in your output; for example, see PICTURE 3 above.
The output will tell you which comparisons are significant. Reporting the results of ANCOVA ANCOVA, or the analysis of covariance, is used when you want to test the main and interaction effect of categorical variables on a continuous dependent variable, while controlling for the effects of other continuous variables or covariates.
For instance, you will use ANCOVA when you want to test whether relationship status categorical independent variable with three levels: You need to report the F values, degrees of freedom for each variable and error , and significance values for both the covariate and the main independent variable. For this, you need to conduct planned contrasts and report the associated significance values for different comparisons.
For instance, you would use MANOVA when testing whether male versus female participants independent variable show a different determination to read a romantic novel dependent variable and a determination to read a crime novel dependent variable.
These tests assess two assumptions: Both tests need to be non-significant in order to assess whether your assumptions are met. If the tests are significant, you need to dig deeper and understand what this means. Following this, you need to report your descriptive statistics, as outlined previously. Here, you are reporting the means and standard deviations for each dependent variable, separately for each group of participants. You will notice that you are presented with four statistic values and associated F and significance values.
These statistics test whether your independent variable has an effect on the dependent variables. You report the results in the same manner as reporting ANOVA, by noting the F value, degrees of freedom for hypothesis and error , and significance value. However, you also need to report the statistic value of one of the four statistics mentioned above. Finally, you need to look at the results of the Tests of Between-Subjects Effects which you will see in your output.
These tests tell you how your independent variable affected each dependent variable separately. Before reporting the results of your qualitative research, you need to recall what type of research you have conducted. The most common types of qualitative research are interviews, observations, and focus groups — and your research is likely to fall into one of these types.
All three types of research are reported in a similar manner. Still, it may be useful if we focus on each of them separately. A photo, for example, should come with the reason why it is there, as well as its source. The most common figures in the Results section are, without a doubt, graphs, as they do a good job in showing connections between data.
Although the choice of using tables or figures is up to the author, a good general recommendation is not using tables when trying to prove a connection between certain groups of values.
If you are writing a paper dedicated to a specific treatment, tables would be used to discuss its cumulative effects, while figures would be used to show each treatment effect variation week by week. Also, avoid adding the same data more than once; this should help keep the Results section brief. Graphics formatting is also important.
Rules for formatting tables and figures vary with each style guide; however, generally, tables have their name and number posted above them and any notes explaining them underneath. The writing in the results section should be kept as simple as possible. If however, an unusual statistical method or model is used, its explanation should be included in the Methodology section.
Although many students are tempted to add explanations or introductory notes to the section, a direct rendition of available data is usually the most recommended approach. This is an example of a text that contains too many useless words and offers a subjective view of the presented data:. Keeping it short is vital. If tables and figures are the main components of the results section, repeating all that info in a text form is redundant.
What you can do through text, though, is pinpointing the most important pieces of data from the tables and figures and using this text to emphasize their importance and relevance to the central idea. Also, all tables and figures included should always be referred to in the text - at least once. A proper context is needed for all included figures, and although no direct interpretation of the data is included in the Results section, the reader must know exactly what he or she is looking at, why it is presented to them, and what it means for the central theme of the paper.
There is no need for a conclusion to the Results section, as you can go directly to the Discussion chapter after completing it. Next, do not forget that writing a Results section of your dissertation is still a very time-consuming task. Even though it may seem that most of the work has already been done, it would still be unwise to underestimate this section. The last and probably the most important tip would be to carefully go over your entire dissertation again - after you finish the results section.
Take a break up to a couple of days if the time allows it and come back to your work with a fresh eye. On this stage, it is vital to double check if all the data in your tables, graphs, and figures were properly referenced in the text. Also, you might want to take another critical look at your paper logic in general to make sure everything is clear and to the point.
Writing your Dissertation Results Section. Academic level Undergraduate Bachelor Professional. Deadline 14 days 10 days 6 days 3 days 2 days 24 hours 12 hours 6 hours 3 hours. Unlock Please, enter correct email. Starting out Organizing the information you have should be the first step toward writing a proper Results section. What needs to be included in the Results section Due to the risk of overwhelming the reader with too many numbers and statistics, your dissertation rarely needs to include pure unedited data.
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Dissertation is a very important type of academic paper and you should be very focused while doing your research. You pay through secure and verified payment systems. Quality. The first thing you need to do in order to write a good Results section is to organize your information. Remember, everything in your results should address the.
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