CHAPTER 3 RESEARCH METHODOLOGY PR 2.pptx

Nature of Quantitative Research
Quantitative research is a systematic investigation that focuses on collecting and analyzing numerical data to explain, predict, or test relationships among variables. It relies on measurable evidence, statistical tools, and structured instruments such as surveys, tests, or experiments. In Practical Research 2, students explore how quantitative methods are used to answer research questions objectively, often through large samples and standardized procedures. This approach emphasizes precision, reliability, and replicability, making it ideal for studies that seek to quantify behaviors, opinions, or outcomes.
Quantitative research is often associated with experimental, descriptive, or correlational designs. It uses tools like graphs, tables, and statistical formulas to present findings clearly and concisely. Whether comparing groups, measuring change, or identifying patterns, this method helps researchers draw conclusions based on observable data.

1. PRACTICAL RESEARCH 2 MS JANET B. DASALLA Presented By

2. Introduction Research Design Respondents of the Study Research Instrument Data Gathering Data Analysis and Procedure CHAPTER 3: RESEARCH METHODOLOGY

3. INTRODUCTION In this chapter, the researcher presents the  blueprint  of the investigation, ensuring that the study is both credible and replicable. It explains the rationale behind choosing specific methods and tools, and how these align with the research objectives. The chapter typically includes: The  research design  (qualitative, quantitative, or mixed methods) The  participants  and  sampling techniques The  data collection instruments  (e.g., surveys, interviews, observations) The  procedures  followed during data gathering The  methods of data analysis

4. RESEARCH DESIGN

5. QUANTITATIVE RESEARCH DESIGN structured approach numerical data objective Measure Variables Statistical Analysis Test Hypotheses Identify relationship Determine Patterns

6. DESCRIPTIVE RESEARCH DESIGN Purpose: To describe characteristics, behaviors, or conditions of a population or phenomenon. Key Features: No manipulation of variables. Focuses on "what is" rather than "why" or "how." Often uses surveys, observations, or existing data. Example: A survey to determine the average number of hours students’ study per week.

7. CORRELATIONAL RESEARCH DESIGN Purpose: To examine the relationship between two or more variables. Key Features: No manipulation of variables. Determine whether variables are related and how strongly Cannot establish causation. Example: Studying the relationship between students’ screen time and their academic performance.

8. EXPERIMENTAL RESEARCH DESIGN Purpose: To establish cause-and-effect relationships by manipulating one variable and observing its effect on another. Key Features: Random assignment of participants to groups Manipulation of the independent variable Control over extraneous variables Example: Testing whether a new teaching method improves test scores compared to traditional methods.

9. QUASI - EXPERIMENTAL RESEARCH DESIGN Purpose: To examine cause-and-effect relationships without random assignment. Key Features: Groups are pre-existing Manipulation of the independent variable Less control over extraneous variables Example: Comparing the effectiveness of two different reading programs in two schools.

10. CAUSAL-COMPARATIVE RESEARCH DESIGN Purpose: To explore cause-and-effect relationships by comparing groups based on pre-existing condition. A.K.A. Ex Post Facto Key Features: No manipulation of the independent variable Groups are formed based on characteristics Attempts to identify possible causes of observed differences. Example: Comparing academic performance between students from single-parent and two-parent households.

11. QUIZ TIME!

12. QUIZ #1 RESEARCH DESIGN Which design involves manipulating variables and using random assignment? a) Experimental b) Descriptive Which design is used to describe characteristics of a population? a) Correlational b) Descriptive Which design examines relationships between variables without manipulation? a) Correlational b) Experimental Which design compares groups based on a pre-existing condition without manipulation? a) Causal-Comparative b) Experimental Which design lacks random assignment but still involves manipulation of variables? a) Quasi-Experimental b) Descriptive

13. QUIZ #1 RESEARCH DESIGN Which design is best for establishing cause-and-effect relationships? a) Experimental b) Correlational Which design is also known as ex post facto research? a) Causal-Comparative b) Quasi-Experimental Which design is most vulnerable to confounding variables due to lack of random assignment? a) Quasi-Experimental b) Experimental Which design is used when researchers cannot ethically manipulate the independent variable? a) Causal-Comparative b) Experimental Which design is used to explore associations but not causation? a) Correlational b) Experimental

14. NULL HYPOTHESIS (Ho) States that no relationship or effect exists between variables. It is what you aim to test and possibly reject. Example: H₀: There is no significant difference in test scores between students who study with music and those who study in silence.

15. ALTERNATIVE HYPOTHESIS (H₁ or Ha) States that a relationship or effect does exist. It is the opposite of the null hypothesis Example: H₁: Students who study with music perform significantly better than those who study in silence.

16. DIRECTIONAL HYPOTHESIS Specifies the direction of the expected relationship (e.g., increase, decrease, higher, lower). Often used when prior research suggests a specific outcome. Example: H₁: Students who sleep more than 8 hours score higher on exams than those who sleep less.

17. NON-DIRECTIONAL HYPOTHESIS Predicts a difference or relationship, but does not specify the direction. Used when there’s no strong theoretical basis for predicting the direction. Example: H₁: There is a significant difference in exam scores between students who sleep more than 8 hours and those who sleep less.

18. DATA TYPE DESCRIPTION EXAMPLE NOMINAL CATEGORIES WITHOUT ORDER GENDER, STRAND ORDINAL ORDERED CATEGORIES SATISFACTION LEVEL, RANK INTERVAL ORDERED, EQUAL INTERVALS, NO TRUE ZERO TEMPERATURE RATIO ORDERED, EQUAL INTERVALS, TRUE ZERO AGE, INCOME TYPES OF DATA

19. DATA TYPE DESCRIPTION EXAMPLE (ABM) NOMINAL CATEGORIES WITHOUT ORDER TOOL TYPE ORDINAL ORDERED CATEGORIES SKILL PROFICIENCY INTERVAL ORDERED, EQUAL INTERVALS, NO TRUE ZERO TEMPERATURE RATIO ORDERED, EQUAL INTERVALS, TRUE ZERO COOKING TIME TYPES OF DATA

20. DESCRIPTIVE PURPOSE: Describe characteristics of a group. DATA: Nominal, Ordinal, Interval, Ratio EXAMPLE: Average Stress Level of HUMSS Students using Likert Scale How Frequent do Grade 11 Students in a Day

21. CORRELATIONAL PURPOSE: Explore relationships between variables. DATA: Ordinal, Interval, Ratio EXAMPLE: Relationship between Financial Literacy and Saving Habits Relationship between Saving Habits and Decision to Bear a Child.

22. CAUSAL-COMPARATIVE PURPOSE: Compare groups based on existing difference DATA: Interval, Ratio EXAMPLE: Motivation levels between TVL Students who does Part-time vs those who didn’t Significant Difference Between thos who attended Tutorials vs those who didn’t

23. RESEARCH INSTRUMENT

24. What are the survey items available? LIKERT SCALE CHECKLIST SURVEYS MULTIPLE CHOICE SURVEYS RANKING SURVEYS SEMANTIC DIFFERENTIAL SCALE DICHOTOMOUS SURVEYS

25. LIKERT SCALE SURVEYS PURPOSE: Measures attitudes, perceptions or opinions. STRUCTURE: Series of Statements with fixed response options (e.g. Strongly Disagree- Strongly Agree) DATA TYPE: Ordinal USE CASE: Measuring motivation, satisfaction, stress, etc.

26. CHECKLIST SURVEYS PURPOSE: Identify the presence or absence of behaviors or experiences. STRUCTURE: List of items where respondents check all that apply. DATA TYPE: Nominal USE CASE: Identifying the common teaching strategies, tools used in workshops etc.

27. MULTIPLE CHOICE SURVEYS PURPOSE: Gather categorical data or preferences. STRUCTURE: Questions with predefined answer options. DATA TYPE: Nominal or Ordinal USE CASE: Demographics, Strand Preferences, Business type etc.

28. RANKING SURVEYS PURPOSE: Determine priority or preference order STRUCTURE: Respondents rank items from most to least preferred DATA TYPE: Ordinal USE CASE: Ranking factors affecting academic performance. Preferred learning styles, etc.

29. SEMANTIC DIFFERENTIAL SCALE PURPOSE: Measures attitude using bipolar adjectives. STRUCTURE: Scale between two opposite adjectives DATA TYPE: Ordinal USE CASE: Ranking factors affecting academic performance, preferred learning styles, etc.

30. DICHOTOMOUS SURVEYS PURPOSE: Get a clear yes/no or true/false response STRUCTURE: Binary options DATA TYPE: Nominal USE CASE: Eligibility Screening, behavior presence, etc.

31. LIKERT - TYPE A single question using a Likert response format. Measures one specific opinion or attitude. Treated as Ordinal Data. Example: “ I feel confident using the app.” (1-5 scale)

32. LIKERT SCALE A group of related Likert-type items that measure a single construct (e.g., motivation, anxiety, satisfaction) Responses are summed or averaged to create a composite score. Treated as interval for statistical analysis.

33. LIKERT SCALE Example: ACADEMIC MOTIVATION: “I enjoy learning new things.” “I feel excited to attend school” “ Iset academic goals for myself.” “I try my best in class activities.” “I feel proud of my academic achievements.”

34. LIKERT VS. LIKERT SCALE FEATURE LIKERT-TYPE LIKERT SCALE STRUCTURE SINGLE ITEM MULTIPLE ITEMS DATA TYPE ORDINAL TREATED AS INTERVAL ANALYSIS FREQUENCIES, MEDIANS AVERAGES, CORRELATIONS, COMPARRISONS EXAMPLE USE DESCRIPTIVE RESEARCH CORRELATIONAL OR CAUSAL-COMPARATIVE RESEARCH

35. TO ACQUIRE LIKERT SCALE SURVEYS YOU CAN… CREATE A NEW SCALE – Review Literature to define the construct and guide item creation. ADAPTING AN EXISTING SCALE – Cite the original source and explain any modifications. USING A PUBLISHED SCALE – Use it as-is and cite it properly in your methodology.

36. MIXED METHOD

37. LIKERT SCALE SURVEYS Data Type: Ordinal (Can be treated as interval if items from a scale. Use Case: Measuring motivation, satisfaction, stress, etc. Descriptive Analysis: Mean, Median, Mode, Frequency Inferential Analysis: Correlation, T-test, ANOVA

38. CHECKLIST SURVEYS Data Type: Nominal Use Case: Identifying common teaching strategies, tools used in workshops etc. Descriptive Analysis: Frequency, Percentage Inferential Analysis: Chi-square test

39. MULTIPLE CHOICE SURVEYS Data Type: Nominal or Ordinal Use Case: Demographics, Strand Preferences, Business type, etc. Descriptive Analysis: Frequency, Mode Inferential Analysis: Chi-square test, Logistic Regression

40. RANKING SURVEYS Data Type: Ordinal Use Case: Ranking factors affecting academic Descriptive Analysis: Median, Mode Inferential Analysis: Friedman Test, Spearman correlation

41. SEMANTIC DIFFERENTIAL SURVEYS Data Type: Measure attitudes using bipolar adjectives Use Case: Scale between two opposite adjectives Descriptive Analysis: Mean, Standard Deviation Inferential Analysis: T-test, ANOVA

42. DICHOTOMOUS SURVEYS Data Type: Nominal Use Case: Eligibility screening, behavior presence etc. Descriptive Analysis: Frequency, Percentage Inferential Analysis: Chi-square, proportion tests.

43. SOME EXAMPLES A teacher wants to explore the relationship between students’ self-esteem and their participation in school organizations. Survey Type: Data Type: Statistical Analysis:

44. SOME EXAMPLES A teacher wants to explore the relationship between students’ self-esteem and their participation in school organizations. Survey Type: Likert Scale (Multiple Items per construct) Data Type: Ordinal (Treated as Interval) Statistical Analysis: Correlational (Spearman or Pearson)

45. SOME EXAMPLES A class wants to compare customer satisfaction between two students-led food stalls. Survey Type: Data Type: Statistical Analysis:

46. SOME EXAMPLES A class wants to compare customer satisfaction between two students-led food stalls. Survey Type: Likert Scale Data Type: Interval Statistical Analysis: Causal-Comparative (Independent samples T-test)

47. SOME EXAMPLES A teacher wants to know which kitchen tools are most commonly used by students in Home Economics. Survey Type: Data Type: Statistical Analysis:

48. SOME EXAMPLES A teacher wants to know which kitchen tools are most commonly used by students in Home Economics. Survey Type: Checklist Data Type: Nominal Statistical Analysis: Descriptive (Frequency, Percentage)

49. SOME EXAMPLES A student wants to rank the most important factors influencing their buying decision. Survey Type: Data Type: Statistical Analysis:

50. SOME EXAMPLES A student wants to rank the most important factors influencing their buying decision. Survey Type: Ranking Data Type: Ordinal Statistical Analysis: Descriptive (Mode, Median) or Friedman Test

51. SOME EXAMPLES A teacher wants to compare hygiene practices between students who had safety training and those who didn’t. Survey Type: Data Type: Statistical Analysis:

52. SOME EXAMPLES A teacher wants to compare hygiene practices between students who had safety training and those who didn’t. Survey Type: Likert Scale Data Type: Interval-like (composite) Statistical Analysis: Causal-Comparative (t-test or ANOVA)

53. SOME EXAMPLES Students from HUMSS 12 wants to explore if those who prefer online learning also report higher satisfaction. Survey Type: Data Type: Statistical Analysis:

54. SOME EXAMPLES Students from HUMSS 12 wants to explore if those who prefer online learning also report higher satisfaction. Survey Type: Multiple Choice + Likert Scale Data Type: Nominal + interval Statistical Analysis: Correlational or Comparative (Chi-square or t-test)

55. SOME EXAMPLES ABM Advisors wants to know if financial literary is related to saving behavior among SHS students. Survey Type: Data Type: Statistical Analysis:

56. SOME EXAMPLES ABM Advisors wants to know if financial literary is related to saving behavior among SHS students. Survey Type: Likert Scale (Financial Literacy, Saving Behavior) Data Type: Interval (composite score) Statistical Analysis: Correlational (Spearman or Pearson)

57. RESEARCH INSTRUMENT Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam,

58. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, Practical Implications Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, Key Takeaways Challenges and Limitations Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation

59. Analysis and Discussion Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam,

60. Conclusion Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit

61. Thank You So Much Ginyard International Co. www.reallygreatsite.com [email protected] @reallygreatsite [phone] Website : Social Media : Phone Number : Email Address :

62. TYPE DESCRIPTION EXAMPLE DESCRIPTIVE DESCRIBE CHARACTERISTICS/BEHAVIOR OF THE POPULATION SURVEY ON STUDENTS’ INTERNET USAGE CORRELATIONAL EXAMINE THE RELATIONSHIPS BETWEEN TWO OR MORE VARIABLES RELATIONSHIP BETWEEN EXERCISE FREQUENCY AND STRESS LEVELS EXPERIMENTAL RESEARCH DETERMINE THE CAUSE-AND-EFFECT RELATIONSHIPS BY MANIPULATING VARIABLES TESTING THE EFFECT OF A NEW TEACHING METHOD ON STUDENTS’ PERFORMANCE DESCRIPTIVE RESEARCH DESIGN

63. TYPE DESCRIPTION EXAMPLE QUASI-EXPERIMENTAL SIMILAR TO EXPERIMENTAL BUT LACKS RANDOM ASSIGNMENT. COMPARING TEST SCORES BETWEEN TWO SCHOOLS USING DIFFERENT CURRICULA CAUSAL-COMPARATIVE DETERMINE THE CAUSE OR REASON FOR EXISTING DIFFERENCES BETWEEN GROUPS. NO MANIPULATION OF INDEPENDENT VARIABLE; GROUPS ARE PRE-EXISTING COMPARING ACADEMIC PERFORMANCE OF STUDENTS FROM SINGLE PARENT VS. PARENT HOUSEHOLDS TYPES OF QUANTITATIVE RESEARCH DESIGN