NATURE AND INQUIRY OF RESEARCH

Lemuel Leonil C. Poculan

At The end of the lesson you will be able to:

1. describes characteristics, strengths, weaknesses, and kinds of quantitative research

2. illustrates the importance of quantitative research across fields

3. differentiates kinds of variables and their uses

QUANTITATIVE RESEARCH

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“Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques. Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon”.

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Babbie, Earl R. The Practice of Social Research. 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS. 2nd edition. London: SAGE Publications, 2010.

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RESEARCH METHODS

• Quantitative Research

• Qualitative Research

WHAT IS QUANTITATIVE RESEARCH?�

• In a nutshell, quantitative research generates numerical data or information that can be converted into numbers.
• Only measurable data are being gathered and analyzed in quantitative research.
• Goal or Aim of the Research:
• The primary aim of a Quantitative Research is to focus more in counting and classifying features and constructing statistical models and figures to explain what is observed.
• Quantitative Research is highly recommended for the late phase of research because it provides the researcher a clearer picture of what to expect in his research compared to Qualitative Research.�

WHAT IS QUANTITATIVE RESEARCH?�

• Data Gathering Instrument
• Quantitative Research makes use of tools such as questionnaires, surveys, measurements and other equipment to collect numerical or measurable data.
• Type of Data
• if you are conducting a Quantitative Research, what will most likely appear in your discussion are tables containing data in the form of numbers and statistics.
•  Approach
• In Quantitative Research, researchers tend to remain objectively separated from the subject matter. This is because Quantitative Research is objective in approach in the sense that it only seeks precise measurements and analysis of target concepts to answer his inquiry.

UNITS AND VARIABLES IN QUANTITATIVE DATA

• Units:

When we collect data in quantitative educational research, we have to collect them from someone or something. The people or things (e.g. schools) we collect data on or from are known as units or cases.

• Variables:

The data that we are collecting from these units are known as variables. Variables are any characteristic of the unit we are interested in and want to collect (e.g. gender, age, self-esteem).

UNITS AND VARIABLES

• The label ‘variable’ refers to the fact that these data will differ between units.

For example, achievement will differ between pupils and schools, gender will differ between pupils, and so on.

• If there are no differences at all between the units we want to study, we probably aren’t going to be able to do any interesting research (for example, studying whether pupils are human would not yield interesting findings).

TYPES OF VARIABLES

• Continuous Discrete
• Interval: 2 variables have meaning (50 degrees Celsius to 30 degrees Celsius)
• Ratio variable: has a clear definition of zero (height, weight, distance and etc.)
• Discrete
• Nominal: two or more categories but not imply ordering of cases. (eye color, religion, business type)
• Ordinal: two or more categories which can be ranked (likeness of music can be categorized from not very much, much or very much])
• Dependent Variables: Outcome or Effect of the result.
• Independent Variables: Cause variable or the one responsible for the conditions to act.

QUANTITATIVE RESEARCH DESIGN

Quantitative research design is

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• They are most commonly used by physical scientists, although social sciences, education and economics have been known to use this type of research. It is the opposite of qualitative research.
• Quantitative experiments all use a standard format, with a few minor inter-disciplinary differences, of generating a hypothesis to be proved or disproved. This hypothesis must be provable by mathematical and statistical means, and is the basis around which the whole experiment is designed.

QUANTITATIVE RESEARCH DESIGN

• Randomization of any study groups is essential, and a control group should be included, wherever possible. A sound quantitative design should only manipulate one variable at a time, or statistical analysis becomes cumbersome and open to question.
• Ideally, the research should be constructed in a manner that allows others to repeat the experiment and obtain similar results.

QUANTITATIVE RESEARCH DESIGN

• There are six types of Quantitative research: 
• Descriptive 
• Correlational
• Evaluation
• Survey
• Causal-Comparative/Quasi-Experimental
• Experimental Research.

DESCRIPTIVE RESEARCH

• This type of research describes what exists and may help to uncover new facts and meaning. The purpose of descriptive research is to

 observe, describe, document

aspects of a situation as it naturally occurs.

• This involves the collection of data that will provide an account or description of individuals, groups or situations. Instruments we use to obtain data in descriptive studies include questionnaires, interviews (closed questions), observation (checklists, etc.)
• There is no experimental manipulation or indeed any random selection to groups, as there is in experimental research.
•  The characteristics of individuals and groups such as nurses, patients and families may be the focus of descriptive research. It can provide a knowledge base which can act as a springboard for other types of quantitative research methods.

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CORRELATIONAL RESEARCH�

• Quantitative correlational research aims to systematically investigate and explain the nature of the relationship between variables in the real world. Often the quantifiable data (i.e. data that we can quantify or count) from descriptive studies are frequently analysed in this way.
• Correlational research studies go beyond simply describing what exists and are concerned with systematically investigating relationships between two or more variables of interest (Porter & Carter 2000).
• Such studies only describe and attempt to explain the nature of relationships that exist, and do not examine causality (i.e. whether one variable causes the other).

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EVALUATION RESEARCH�

• This kind of research aims to assess the effects, impacts or outcomes of practices, policies or programs. Assessing the implementation of nursing care in a hospital and determining the impact of a treatment procedure for patients are examples of evaluation research.

SURVEY RESEARCH

• This research is used to gather information from groups of people by selecting and studying samples chosen from a population. May be done by various ways like face to face, phone, mail and online.
• Cross-sectional if information is collected from a sample in just single point in time just like a child rearing practices of single parents and population control practices of unmarried couples.

CAUSAL-COMPARATIVE/QUASI-EXPERIMENTAL RESEARCH

• Also called ex post facto (after the fact) research
• Derives conclusion from observations and manifestations that already occurred in the past now compared to some dependent variables.
• Discusses why and how phenomenon occurs.
• Ex: how weight influences stress-coping level of adults. Subjects can be separated into different groups (overweight, underweight, and normal) and their stress-coping levels measured.

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EXPERIMENTAL RESEARCH

• often called true experimentation, uses the scientific method to establish the cause-effect relationship among a group of variables that make up a study. 
• The true experiment is often thought of as a laboratory study, but this is not always the case; a laboratory setting has nothing to do with it.  A true experiment is any study where an effort is made to identify and impose control over all other 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

WHAT IS THE BASIC METHODOLOGY FOR A QUANTITATIVE RESEARCH DESIGN?

The overall structure for a quantitative design is based in the scientific method.  It uses deductive reasoning, where the researcher forms an hypothesis, collects data in an investigation of the problem, and then uses the data from the investigation, after analysis is made and conclusions are shared, to prove the hypotheses not false or false.  The basic procedure of a quantitative design is:

1. Make your observations about something that is unknown, unexplained, or new.  Investigate current theory surrounding your problem or issue. 
2. Hypothesize an explanation for those observations.
3. Make a prediction of outcomes based on your hypotheses. Formulate a plan to test your prediction.
4. Collect and process your data. If your prediction was correct, go to step 5. If not, the hypothesis has been proven false. Return to step 2 to form a new hypothesis based on your new knowledge.�
5. Verify your findings.  Make your final conclusions.  Present your findings in an appropriate form for your audience.

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ADVANTAGES OF QUANTITATIVE RESEARCH

• Quantitative research design is an excellent way of finalizing results and proving or disproving a hypothesis. The structure has not changed for centuries, so is standard across many scientific fields and disciplines.
• After statistical analysis of the results, a comprehensive answer is reached, and the results can be legitimately discussed and published.
• Quantitative experiments also filter out external factors, if properly designed, and so the results gained can be seen as real and unbiased.
• Quantitative experiments are useful for testing the results gained by a series of qualitative experiments, leading to a final answer, and a narrowing down of possible directions for follow up research to take.�

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DISADVANTAGES OF QUANTITATIVE RESEARCH

• Quantitative experiments can be difficult and expensive and require a lot of time to perform.
• They must be carefully planned to ensure that there is complete randomization and correct designation of control groups.
• Quantitative studies usually require extensive statistical analysis, which can be difficult, due to most scientists not being statisticians. The field of statistical study is a whole scientific discipline and can be difficult for non-mathematicians
• The requirements for the successful statistical confirmation of results are very stringent, with very few experiments comprehensively proving a hypothesis; there is usually some ambiguity, which requires retesting and refinement to the design. This means another investment of time and resources must be committed to fine-tune the results.
• Quantitative research design also tends to generate only proved or unproven results, with there being very little room for grey areas and uncertainty. For the social sciences, education, anthropology and psychology, human nature is a lot more complex than just a simple yes or no response.

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CHARACTERISTICS OF QUANTITATIVE RESEARCH

Its main characteristics are:

• The data is usually gathered using structured research instruments.
• The results are based on larger sample sizes that are representative of the population.
• The research study can usually be replicated or repeated, given its high reliability.
• Researcher has a clearly defined research question to which objective answers are sought.
• All aspects of the study are carefully designed before data is collected.
• Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
• Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
• Researcher uses tools, such as questionnaires or computer software, to collect numerical data.
• The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.