Competency: Presents and interprets data in tabular and graphical forms with implications and citations.
Presentation, Analysis, and Interpretation of Data
Presentation is the process of organizing data into logical, sequential, and meaningful categories and classifications to make them amenable to study and interpretation.
Three Ways of Presenting Data
1. Textual – statements with numerals or numbers that serve as supplements to tabular presentation
2. Tabular – a systematic arrangement of the related idea in which classes of numerical facts or data are given each row, and their subclasses are given each a column in order to present the relationships of the sets of numerical facts or data in a definite, compact and understandable form.
Two general rules regarding the independence of tables and text
a) The table should be so constructed that it enables the reader to comprehend the data presented without referring to the text;
b) The text should be so written that it allows the reader to understand the argument presented without referring to the table (Campbell, Ballou, and Slade, 1990)
3. Graphical – a chart representing the quantitative variations or changes of variables in pictorial or diagrammatic form.
Types of Graphs and Charts
1. Bar graphs
2. Linear graphs
3. Pie charts
4. Pictograms
5. Statistical maps
6. Ratio charts
Analysis of Data
Separation of a whole into its constituent parts (Merriam-Webster, 2012). The process of breaking up the whole study into its constituent parts of categories according to the specific questions under the statement of the problem (Calderon, 1993).
Two Ways of Data Analysis
1. Qualitative Analysis – is not based on precise measurement and quantitative claims.
Examples of Qualitative Analysis:
a) Social analysis;
b) From the biggest to the smallest class;
c) Most important to the least important;
d) Ranking of students according to brightness;
2. Quantitative Analysis – is employed on data that have been assigned some numeral value.
It can range from the examination of simple frequencies to the description of events or phenomenon using descriptive statistics, and to the investigation of correlation and causal hypothesis using various statistical tests.
Interpretation of Data
It is often the most difficult to write because it is the least structured. This section demands perceptiveness and creativity from the researcher.
How do we Interpret the Result(s) of our Study?
1. Tie up the results of the study in both theory and application by pulling together the:
a. Conceptual/ theoretical framework;
b. The review of literature; and
c. The study’s potential significance for application
2. Examine, summarize, interpret and justify the results; then, draw inferences. Consider the following:
a. Conclude or summarize – this technique enables the reader to get the total picture of the findings in summarized form, and helps orient the reader to the discussion that follows.
b. Interpret – questions on the meaning of the finding, the methodology, the unexpected results and the limitations and shortcomings of the study should be answered and interpreted.
c. Integrate – This is an attempt to put the pieces together. Often, the results of the study are disparate and do not seem to “hang together.” In the discussion, attempt to bring the findings together to extract meaning and principles.
d. Theorize – when the study includes a number of related findings, it occasionally becomes possible to theorize.
* Integrate your findings into a principle:
* Integrate a theory into your findings; and
* Use these findings to formulate an original theory
e. Recommend or apply alternatives
Level of Significance
The significance level denoted as alpha or α is a measure of the strength of the evidence that must be present in your sample before you reject the null hypothesis and conclude that the effect is statistically significant. The researcher determines the significance level before conducting the experiment.
The significance level is the probability of rejecting the null hypothesis when it is true. For example, a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference. Lower significance levels indicate that you require stronger evidence before you will reject the null hypothesis.
Use significance levels during hypothesis testing to help you determine which hypothesis the data support. Compare your p-value to your significance level. If the p-value is less than your significance level, you can reject the null hypothesis and conclude that the effect is statistically significant. In other words, the evidence in your sample is strong enough to reject the null hypothesis at the population level.
In Deducting Interpretation from Statistical Analysis, the Following Key Words or Phrases may be Useful:
1. Table ___ presents the…
2. Table ___ indicates the …
3. As reflected in the table, there was…
4. As observed, there was indeed…
5. Delving deeper into the figures…
6. The illustrative graph above/below shows that…
7. In explaining this result, it can be stated that…
8. Is significantly related to…
9. Is found to be determinant of…
10. Registered positive correlation with…
11. Is revealed to influence…
12. Has significant relationship with…
13. Is discovered to be a factor of…
14. In relation with the result of ____, it may be constructed that…
15. And in viewing in this sense, it can be stated that…
16. The result establishes the fact that…
17. This finding suggests that…
18. With this result, the researcher developed an impression that…
19. This finding also validates the findings of…
20. This improvement in ____ could be understood in the context of…
21. These findings also accept the framework of the study…
22. The interpretation marked as ____ reveals that…
23. Nevertheless, this finding could be attributed to the fact that…
24. Probably, this was also influenced…
25. In the rational sense, the juxtaposition of…