PR2 Module 2.pdf

QUANTITATIVE RESEARCH Results and Discussion Prepared by: Illuminada S. Menzi OBJECTIVES 1. collect data using appropriate instruments; 2. present and interpret data in tabular form; and 3. use statistical techniques to analyze data – study of differences and relationships limited fo...

QUANTITATIVE RESEARCH Results and Discussion Prepared by: Illuminada S. Menzi OBJECTIVES 1. collect data using appropriate instruments; 2. present and interpret data in tabular form; and 3. use statistical techniques to analyze data – study of differences and relationships limited for bivariate analysis.  QUANTITATIVE DATA COLLECTION TECHNIQUES Data collection the process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer stated research questions, test hypotheses and evaluate outcomes.  QUANTITATIVE DATA COLLECTION TECHNIQUES MOST FREQUENTLY USED DATA COLLECTION TECHNIQUES Methods that researchers use in collecting desired data is called measurement instrument. 1. Observation Using sense organs to gather facts or information about people, things, places, events by watching and listening to them. Direct Indirect QUANTITATIVE DATA COLLECTION TECHNIQUES 2. Survey ▪ Questionnaire ▪ Structured questionnaires – provide possible answers and respondents just have to select from them. ▪ Unstructured questionnaires – do not provide options and the respondents are free to give whatever answer they want. 5 QUANTITATIVE DATA COLLECTION TECHNIQUES Types of Questionnaire 1. Test ▪ A test is a tool used to assess the knowledge of the respondents. In doing test, there is a usual time limit to take it. ▪ Example: Pre-test, Post Test, Aptitude Test, etc. 6 QUANTITATIVE DATA COLLECTION TECHNIQUES 2. Checklist ▪ A checklist is a comprehensive list that allows the respondents to give a multiple answer. 7 QUANTITATIVE DATA COLLECTION TECHNIQUES 3. Point-scale system ▪ A point scale system is a tool used to determine the level of a specific measurement. ▪ The most commonly used scale is the 5 – point scale or also known as the Likert scale. 8  QUANTITATIVE DATA COLLECTION TECHNIQUES 3. Experiment ▪ Situation in which variables are controlled and manipulated to establish cause-and-effect relationships. 9 CHARACTERISTICS OF A GOOD DATA COLLECTION INSTRUMENT: ▪ It must be concise yet able to elicit the needed data. ▪ It seeks information which cannot be obtained from other sources like documents that are available at hand. ▪ Questions must be arranged in sequence, from the simplest to the 10complex. CHARACTERISTICS OF A GOOD DATA COLLECTION INSTRUMENT: ▪ It must also be arranged according to the questions posed in the statement of the problem. ▪ It should pass validity and reliability. ▪ It must be easily tabulated and interpreted. 11  COMMONLY USED TOOLS OF DATA PRESENTATION 1. Tables ▪ Provide exact Tablevalues number and illustrate results efficiently and Title Caption subhead 18 Body Elrod, Emily, and Joo Young Park. (2020). A Comparison of Students’ Quantitative Reasoning Skills in STEM and Non-STEM Math Pathways. Numeracy 13(2). DOI: https://doi.org/10.5038/ 1936-4660.13.2.1309  COMMONLY USED TOOLS OF DATA PRESENTATION Elrod, Emily, and Joo Young Park. (2020). A Comparison of Students’ Quantitative Reasoning Skills in STEM and Non-STEM Math Pathways. Numeracy 13(2). DOI: https://doi.org/10.5038/ 1936-4660.13.2.1309 Table 2 shows the percentage of females and males who displayed the pattern SATQ > SATV. A minority of females had this pattern (40.2%), whereas the majority of males 19 (55.8%) displayed the pattern. Within every major, the proportion of females displaying the pattern was lower than the proportion of males. Our hypothesis that SATQ > SATV is less common among females was supported.  COMMONLY USED TOOLS OF DATA PRESENTATION 2. Graph ▪ shows relations, comparisons, and distributions in a set of data like absolute values, percentages, or index numbers 20 PRESENTATION AND INTERPRETATION OF RESULTS  MAJOR ELEMENTS OF THE SECTION: ▪ Presentation of data ▪ Analysis ▪ Interpretation ▪ Discussion 22  PRESENTATION OF DATA IN TABULAR AND 23 GRAPHICAL FORM  Introducing graphs or tables: The pie graph presented in Figure 2 shows the percentage of enrolled Grade 11 senior high school students for school year 2014- 2015. 24 Fig. 2. Percentage of Grade 11 Students enrolled per strand for the SY 2014-2015.  Ways of introducing graphs or tables: The pH values of samples taken at each station during the period of experiment were measured and presented in Table 1. ▪ Olubanjo, O. & Adeleke, E. (2020). Assessment of Physico- chemical Properties and Water Quality of River Osse, Kogi State. Applied Research Journal of Environmental Engineering 3(1),21-30. 25 ANALYSIS OF DATA In analyzing the data, the following must be considered: ▪ The highest numerical value such as scores, weighted means, percentages, variability, etc. ▪ The lowest numerical value such as scores, weighted means, percentages, variability, etc. ▪ The most common numerical values like mode or values that appear repeatedly ▪ The final numerical value like the average weighted mean, total, correlation index, etc. ANALYSIS OF DATA Akgunduz, D. (2015) A Research about the Placement of the Top Thousand Students in STEM Fields in Turkey between 2000 and 2014. Eurasia Journal of Mathematics, Science & Technology Education, 2016, 12(5), 1365-1377 doi: 10.12973/eurasia.2016.1518a ANALYSIS OF DATA INTERPRETATION OF DATA The following are the levels of interpretation which are considered in organizing the discussion of the results of findings (Ducut and Pangilinan, 2006): Level 1 Data collected are compared and contrasted. Unexpected results if any may be mentioned. Level 2 The researcher should explain the internal validity of the results as well as their consistency or reliability. The causes or factors that may have influenced the results may also be described. Level 3 The researcher should explain the external validity of the results, that is, their generality or applicability to external conditions. Level 4 The researcher should relate or connect the interpretation of 29 data with theoretical research or with the reviewed literature. (corroboration) DISCUSSION OF DATA 1. The flow of the discussion of results or findings is based on how the problems are stated. 2. The manner or sequence of discussion should include the following: a. Discussion of the findings in relation to the results of previous studies cited in the review of related literature and studies (Corroboration) 30 b. Implications, inferences, and other important information INTERPRETATION AND DISCUSSION ANALYSIS OF DATA CORROBORATION: REVIEW to enhance validity, reliability, authenticity, replicability, and accuracy of the research. necessary to maintain standards in conducting surveys, data analysis, and interpretation. Approaches to corroboration REVIEW 1. Supporting documents/proofs 2. Other data sources 3. Consistency check 4. Comparing results to similar studies Approaches to corroboration REVIEW Relationship to findings of other research ▪ Similarities to previous findings ▪ Differences from previous findings ▪ Possible reasons for similarities and differences