1. Finding Answers through Data Collection
Session 4:
Finding Answers through Data Collection
SHS Mass Training of Teachers, Practical Research 2

2. Standards and Competencies
Finding Answers through Data Collection
Content Standards: The learner demonstrates understanding of: (1) data collection procedures and skills using varied instruments; (2) data processing, organizing, and analysis.
Performance Standards: The learner is able to gather and analyze data with intellectual honesty, using suitable techniques
Learning Competencies
The learner…
Collects data using appropriate instruments
Presents and interprets data in tabular and graphical forms
Use statistical techniques to analyze data – study of differences and relationships limited for bivariate analysis
This slideshow presentation will be made available through the trainer’s website: mathbychua.weebly.com.
Download the document to use it as reference.

3. Chapter IV PRESENTATION AND ANALYSIS OF DATA
Chapter Content
Chapter IV PRESENTATION AND ANALYSIS OF DATA
In reporting the results, the researcher stays close to the statistical findings without drawing broader implications or meaning from them. Further, this section includes summaries of the data rather than the raw data (e.g., the actual scores for individuals). A results section include tables, figures, and detailed explanations about the statistical results

4. Rewriting Initial Chapters
Before writing this section,
Rewrite Chapters 1-3 before or after data analysis and before writing Chapter 4.
Rewrite Chapters in past tense, wherever applicable, and make corrections for actual data collection and data analysis procedures.
Rewriting Initial Chapters

5. Preparing Data for Analysis1
Score the data (Coding)
Select a statistical program (e.g. SPSS).
Input the data
Clean and account for missing data. 2 3 4
Assigning a numeric score to each response category for each question in the instrument used
You do not need to score all the data you gathered, only the variables that you want to treat statistically with other variables either through correlation, regression, or comparative analysis. Nominal and ordinal data need to be artificially quantified. For example, if you want to determine if sex has any effect on competency, assign 1 for male and 2 for female. Likert scales are also very common in research. Developed by Rensis Likert in 1938, the usual 5-point or 7-point ordinal scale allows respondents to determine the level by which they agree or disagree with something.
When it comes to choosing a statistical program, we are very fortunate that at this day and age, we have countless options. Reliability is a big question however as some of the online statistics calculators can sometimes be questionable. The most common software we can use is the Microsoft excel but the most recommended for researchers is SPSS. SPSS is unfortunately for us, not a freeware.
Cleaning and accounting for missing data is very important. If during the course of data analysis the researcher notices that there is any disparity in the result of data gathering, a decision needs to be acted upon. The researcher may choose to eliminate the participants whose responses are questionable and may actually hinder the success of the research or fill in the gaps in the data with values that will not affect the rest of the data.
[EXPERIENCE]
Preparing Data for Analysis

6. How to Analyze Quantitative Data1
Describe trends in the data to a single variable or question on your instrument.
Compare two or more groups on the independent variable in terms of the dependent variable.
Relate two or more variables.
Test hypothesis about the differences in the groups or the relationships of variables. 2 3 4
How to Analyze Quantitative Data

7. How to State Results In writing this chapter,
Label section headings based on research questions and follow their sequence
Determine parts of the data you collected that focused on each of the variables. One subsection should be devoted to presenting data relevant to each variable. Use tables and graphs in presenting statistical data.
Put greater emphasis on significant results. Results that are sidelights should not receive equal weight.
Reexamine your research question and identify the important variables included in the research.
How to State Results

8. Do not state any differences were present between groups unless a significant p-value is attached.
Do not comment on results.
Select statistics and place them in the most reasonable order
Include
tables, graphs, figures for quantitative study
statistical results of hypotheses tested
possible explanations of unexpected results
Implications of results to existing situation
How to State Results

9. Quantitative data are organized & summarized in tables and figures.
Information shown in tables and figures is elaborated in the text.
Present results of hypothesis test in tables using statistical format, e.g., t value and level of significance (p-value).
Tables and Figures

10. To compare proportions and relative amounts (How big
To compare proportions and relative amounts (How big?), use a pie chart, a horizontal bar chart, or a table
Choosing Graph Types

11. Choosing Graph Types
To show trends (How do things change over time?), use a column chart or line graph

12. Choosing Graph Types
To show correlations (how well does one thing predict another?), use a scatterplot or multiplot chart.

13. Results of Hypothesis Testing
Enrich and support quantitative analysis with qualitative data analysis using selected quotes/utterances & observed behavior of subjects.
Results of Hypothesis Testing

14. Results of Related Studies
Compare results of present study with related studies, as follows:
This result
is consistent with or similar to that of… (related study)
contradicts/opposes that of… (related study)
Results of Related Studies

15. Possible Explanations
Needed especially when results are not those expected by researcher.
Cite all things that happened that may have affected findings.
Look at limitations of the study, e.g., in sampling, instrumentation, intervention, and data collection.
Possible Explanations

16. Possible Explanations
Was the conduct of research not implemented as planned? Were there deviations from planned procedure?
Was dosage or duration of experiment of intervention sufficient?
Was there a contamination of comparison group?
Are there characteristics of sample that affected the results?
Were there issues with the instruments’ validity and reliability? in test administration?
Was there any instance of resistance of sample to intervention?
Possible Explanations

17. If some or all findings are different from expected, have a section on re- conceptualized framework at end of Chapter 4 and make diagram of re-conceptualized framework.
Starting with text, cite figure (diagram) of re- conceptualized framework. Explain changes vis-à-vis Conceptual Framework of the Study.
Re-conceptualized Framework

18. PRESENTATION AND ANALYSIS OF DATA
WORKSHOP, CHAPTER FOUR
PRESENTATION AND ANALYSIS OF DATA
Come up with a fabricated data for your research based on your Statement of the Problem and write briefly this chapter