PR2 (1st Preliminary)

(Lesson 1 and 2)

Inquiry

• Is a broader, more general process of asking questions and seeking information to understand something

Research

• Is a more formal and systematic process of investigation

Qualitative Research

• Collects non-numerical data to understand or explain concepts or phenomenon

Quantitative Research

• Focuses on analysis of numerical data and form conclusions from samples drawn from the population

• It contains measurable variables.

• It uses standardized research instruments such as tests, scales, and surveys.

• It generalizes conclusions from samples drawn from a population.

• It presents data in tabular, graphical, or figure form.

• It uses statistics to analyze data.

• It is objective and logical.

• It follows the scientific method.

Characteristics of Quantitative Research

It contains measurable variables.

• Focuses on variables that can be measured numerically. 

It uses standardized research instruments such as tests, scales, and surveys.

• Involves the use of standardized instruments, those have been proven to be reliable and valid.

It generalizes conclusions from samples drawn from a population.

• Is associated with hypothesis testing and statistics. 

It presents data in tabular, graphical, or figure form.

• Contains graphical representations to better understand the content of a research study.

It uses statistics to analyze data.

• The statistical computations aid the researchers to interpret and draw conclusions accurately.

It is objective and logical. 

• Bias is avoided by minimizing the impacts of extraneous variables, such as emotions or differing perspective in the use of words.

It follows the scientific method. 

• Everything starts from observation and identification of problems, then gather the needed data, and ends with a conclusion. 

Tally and Frequency Table

• Simple way of recording the results of a survey

Grouped Frequency Table

• The groups are chosen so that no data item can appear in two groups.

• Pictogram

• Bar Graph or Bar Diagram

• Bar Line Graph

• Pie Chart

Displaying data

Pictogram

• This method uses pictures to represent the frequency.

Bar Graph or Bar Diagram

• Most common way of displaying data.

Bar line Graph

• Lines are drawn to represent frequency.

Pie Chart

• Divided into slices which are fractions of the circle.

• The size of each slice represents the frequency.

• Descriptive

• Correlational

• Causal-Comparative

• Experimental

• Quasi-Experimental

Kinds of Research According to Purpose

Descriptive

• Describe the characteristics of a population, event, or phenomenon.

• Answers the “what is” question.

Correlational

• Examines the relationship between two or more variables without manipulating them.

Causal-Comparative

• Determine the cause or reason for existing differences between groups.

• Also known as “ex post facto”.

Experimental

• Involves manipulation of variables to determine cause-and-effect relationships

• uses controlled environment and random assignments

Quasi-Experimental

• Investigates cause and effect relationships

• Lacks or no random assignment.

• Qualitative

• Quantitative

Kinds of Research According to the Type of Data Used

Variables

• Measurable attributes or characteristics.

• Serves as a basis for collecting, organizing, presenting, analyzing, and interpreting data in quantitative research.

• Independent

• Dependent

• Extraneous

• Intervening

• Antecedent

Types of Operational Variables

Independent Variables

• Variables that causes change.

Dependent Variables

• Effects brought by the Independent Variable.

Extraneous Variables

• Factors that are not the focus but could affect the results if not controlled.

Intervening Variables

• Linking the dependent and independent.

Antecedent Variables

• Variables that come before both the independent and dependent.

• Categorical (Quali)

  • Nominal

  • Ordinal

• Numerical (Quanti)

  • Interval

  • Ratio

Measuring Variables

Categorical Variables

• Those that may be classified into categories or may be placed in rank orders. 

• Variable that puts data into groups or categories.

  Example:

  • Favorite color (red, blue, green)

  • Type of pet (dog, cat, bird)

Nominal Variables

• Enable the classification of individuals, objects, or responses based on a common property or characteristic.

• Data that is named or labeled but has no order.

  Example:

  • Types of fruit: apple, banana, mango

  • Gender: male, female

Ordinal Variables

• Have all the characteristics of a nominal variable but ranked in a certain order. 

• Data that can be ordered or ranked, but the differences between them are not equal.

  Example:

  • Social class: upper, middle, low

  • Class rank: 1st, 2nd, 3rd

Numerical Variables

• Those that have numerical values or measures.

• Variable that uses numbers to measure something.

  Example:

  • Height in centimeters

  • Number of books read

Ratio Variables

• Based on a fixed starting point or a “true zero point”.

• Data with equal intervals and a true zero, so you can compare and do all math operations.

  Example:

  • No. of students per classroom: 38, 47, 54

  • Annual family income (in pesos): PHP 1200, PHP 2750

Interval Variables

• Describe a variable whose attributes are rank-ordered and have equal distances between adjacent attributes.

• Data with equal spaces between values, but no true zero.

  Example:

  • Temperature in Celsius: 10°C, 20°C, 30°C

  • Dates on a calendar

Numerical

• Can count or measure it.

Categorical

• Can name or label it.

Nominal

• Name only.

Ordinal

• Order matters.

Interval

• order + equal gaps (no true zero)

Ratio

• order + equal gaps + true zero

• Classifying

• Measuring

• Explaining

• Assessing Relationships

Uses of Variables in Quantitative Research

Classifying

• Using variables to group or sort things into categories.

  Examples:

  • Grouping students by learning style (visual, auditory, kinesthetic).

  • Classifying the student body according to demographic profile (gender, age, educational level)

Measuring

• Using variables to count or measure something with numbers.

  Example:

  • Measuring students’ test scores out of 100.

Explaining

• Using variables to explain why something happens.

  Example:

  • Explaining how study habits affect academic performance.
    

Assessing Relationships

• Using variables to see if there’s a connection between two or more things.

  Example:

  • Studying the relationship between hours of sleep and energy level.

Research Title

• Most important element that defines the research problem.

• Contains the least words enough to describe the contents and the purpose of your research paper.

Nature of data

• Involves numerical data.

• Can be counted, meaured, and quantified.

Purpose

• Aims to quantify the problem by generating numerical data.

Data Collection Method

• Uses structured tools such as surveys, experiments, and questionnaires.

Outcome

• Produces concrete numerical results that can be generalized to a larger population.

• Subject matter

• Population

• Place or locale

• Time period

Elements of Research Title

Subject matter

• Answers “WHAT”?

Population

• Answers “WHO”?

Place or locale

• Answers “WHERE”?

Time period

• Answers “WHEN”?

1.  Interest in the subject matter

2. Availability of Information

3. Timeliness and Relevance of the Topic

4. Limitations on the subject

5. Personal Resources

Rules In Choosing a Research Topic

1. Controversial Topics

2. Highly Technical Subjects

3. Hard-to-Investigate Subjects

4. Too Broad Subjects

5. Too Narrow Subjects

6. Vague Subjects

Research Topics to be Avoided

• Mass Media Communication

•  Books, Internet, peer-reviewed journals, government publications

• Professional periodicals

• General periodicals

• Previous reading assignments

• Work experience

  
  

Sources of Research Topics