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Quantitative Research

• It is an objective, systematic investigation of observable phenomena through computational techniques.

• It highlights numerical analysis of data in the hope that the numbers yield unbiased results that can be generalized to some larger population.

• It is concerned with numbers and their relationships to events.

• Best for problems that require measurement and quantification.

• Suitable for investigating human, social, behavioral, or organizational aspects that can be measured.

Stages of Research

1. Identify the research problem and formulate research questions.

2. Review related literature.

3. Formulate frameworks and hypotheses.

4. Choose data collection and analysis procedures.

5. Collect data.

6. Analyze data.

7. Conclude and make recommendations.

Research Design

• A research design is an overall strategy that integrates the components of a study.

Types of Quantitative Research

• Descriptive: Describes the current status of an identified variable. Provides systematic information about a phenomenon. The researcher does not usually begin with a hypothesis, but may develop one after collecting data.

• Correlational: Determines the extent of a relationship between two or more variables using statistical data. It can recognize trends and patterns in data, but does not prove causes for them. Cause and effect is not the basis of this type of observational research. Variables are not manipulated; they are only identified and are studied as they occur in a natural setting.

• Causal-Comparative / Quasi-Experimental: Attempts to establish cause and effect relationships among the variables. Similar to true experiments but an independent variable is identified but not manipulated by the experimenter, and the effects on the dependent variable are measured.

• Experimental (Empirical): Often called true experimentation. Uses the scientific method to establish the cause-effect relationship among a group of variables. Effort is made to identify and impose control over all variables except one. An independent variable is manipulated to determine the effects on the dependent variables. Subjects are randomly assigned to experimental treatments rather than identified in naturally occurring groups.

Key Concepts in Experimental Research:

• Independent variable (CAUSE): The variable manipulated or controlled by the experimenter.

• Dependent variable (EFFECT): The factor that is predicted to be affected by the independent variable.

• Control group: Subjects not exposed to the experimental treatment.

• Experimental group: Subjects receiving the experimental treatment.

Locale of the Study

• Research locale refers to the specified area and/or subject being studied. Also known as the setting of the study.

Research Respondents

Sampling Methods

• Probability Sampling: Uses probability theory to filter individuals from a population to create samples. Participants of a sample are chosen through random selection. Each member of the target audience has an equal opportunity to be selected.

  • Simple random sampling: Random selection of elements for a sample from a large target population.

  • Stratified random sampling: A large population is divided into groups (strata), and members of a sample are chosen randomly from these strata.

  • Cluster sampling: The main segment is divided into clusters, usually geographically or demographically.

  • Systematic sampling: The starting point of the sample is chosen randomly, and all other elements are chosen using a fixed interval.

• Non-probability Sampling: The researcher’s knowledge and experience are used to create samples. Not all members of the target population have an equal probability of selection.

  • Convenience sampling: Elements of a sample are chosen only due to their proximity to the researcher.

  • Consecutive sampling: Similar to convenience sampling, but researchers can choose a single element or group of samples and conduct research consecutively over time.

  • Quota sampling: Researchers select elements using their knowledge of target traits and personalities to form strata.

  • Snowball sampling: Conducted with target audiences that are difficult to contact.

  • Judgmental sampling: Samples are created based only on the researcher’s experience and skill.

Research Resources

• Primary: In sciences, the first report of research, such as journal articles and research reports. In social sciences, it may be the first report of research or resemble primary sources in history, relying on direct observation and personal narratives. In history, it may include letters, diaries, and other firsthand accounts. In the arts, it is a piece of art created by the artist.

• Secondary: Synthesizes information from other sources, such as books and articles. Examples are textbooks and review articles.

• Tertiary: Aids in locating primary or secondary sources. Examples are bibliographies, databases, and indexes.

Philosophical Approaches in Quantitative Research:

• Realism: Claims that whatever exists can be verified through observation, experiment, and mathematical and logical evidence.

• Positivism: Claims that the world exists independent of the mind.

• Empiricism: A philosophical view that all concepts originate from experience. What cannot be observed cannot be reasonably studied.

Data Collection Techniques

1. Interview: Can be structured, semi-structured, or informal.

2. Questionnaires and surveys: Responses can be analyzed with quantitative methods.

3. Observations: Allows for the study of the dynamics of a situation.

4. Focus Groups: A facilitated group interview with individuals who have something in common.

5. Documents and Records: Examining existing data in the form of databases, minutes, reports, etc.

Collecting Quantitative Data

1. Know the participants and explain their selection and contribution to the study.

2. Seek necessary permissions.

3. Identify means of collecting data.

4. Identify, locate, and choose an instrument for data collection.

5. Describe data collection procedures.

Research Instrument

• Validity: The degree to which an instrument measures what it intends to measure.

  • Content Validity: The extent to which the content of the test is representative of the content of the course.

  • Criteria or Concurrent Validity: The degree to which the test agrees with a criterion set up as an acceptable measure.

  • Predictive Validity: Determined by showing how well predictions made from the test are confirmed by evidence gathered at some subsequent time.

• Reliability: The extent to which a test is dependable, self-consistent, and stable. It is concerned with the consistency of responses. A reliable test may not always be valid.

• Cronbach's alpha: The most common measure of internal consistency ("reliability"). Used to determine the reliability of scales formed by multiple Likert questions. Cronbach's alpha is a measure of reliability, not validity. Values range from 0 to 1, with 0.70 to 1.00 being acceptable. Values below 0.70 are unacceptable.

• Usability: The degree to which the research instrument can be satisfactorily used without undue expenditure of time, money, and effort.

Likert Scale

• Commonly used in social science research to measure attitudes, opinions, and perceptions.

• Consists of a series of items or statements and a set of response options arranged in a range from strongly disagree to strongly agree.

Types of Survey Questions:

1. Open-Ended Questions: Do not have predetermined options. Responses are recorded verbatim.

2. Dichotomous Questions: Have two possible answers, such as YES/NO or TRUE/FALSE.

3. Multi-Response Questions: Require respondents to provide more than one answer.

4. Matrix Questions: Have the same possible answers for multiple items.

5. Contingency Questions: Intended for respondents who meet certain criteria.

Data Analysis

• Statistics: The systematic collection and analysis of data. Tables, graphs, and diagrams are visual representation tools.

Types of Statistics:

1. Descriptive Statistics: Describes, summarizes, and traces basic patterns of a particular trait in a sample. Presents quantitative data in manageable forms. Uses summary measures and graphing techniques to organize and summarize data. Descriptive statistics do not allow conclusions beyond the data analyzed.

2. Inferential Statistics: Makes inferences or generalizations about a larger population from which a sample is drawn. It is also known as inductive statistics.

Levels of Measurement:

• Nominal: Identifies variables with values that have no mathematical interpretation. Variables vary in kind or quality, not in amount. Cannot be quantified or subjected to arithmetic operations. Examples include gender, hair color, and religion.

• Ordinal: Numbers assigned to cases specify only the order of the cases. The distance between categories does not matter, but they can be rank-ordered. Examples include class ranking and socioeconomic status.

• Interval: Represents fixed measurement units but has no absolute zero point. The gaps between the numbers on the scale are meaningful. Examples include temperature and IQ.

• Ratio: Represents fixed measuring units and an absolute zero point. Zero means the absence of the attribute. Examples include age, weight, and height.

Scales and Indexes:

• Binary Scale: A nominal scale with binary items that have one of two possible values.

• Likert Scale: Uses simple worded statements to which respondents can indicate their extent of agreement or disagreement.

• Semantic Differential Scale: A composite scale where respondents indicate their opinions using pairs of adjectives framed as opposites.

• Guttman Scale: A multi-item scale that uses items arranged in increasing order of intensity. Guttman scales are interval/ratio.

• Index: A composite score derived from aggregating measures of multiple constructs. It measures different dimensions or the same dimension of a single construct.

Planning Data Analysis

• Outlines how data will be analyzed to answer the research questions.

• Considers how data can be analyzed to answer the research questions.

• Descriptive research utilizes descriptive statistics and visual tools such as tables, graphs, and charts. These tools help summarize, organize, and present information clearly.