Quantitative Research

                                                Definition

Quantitative research is expressed in numbers and graphs. It is used to test or confirm theories and assumptions. This type of research can be used to establish generalizable facts about a topic. Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning.

Characteristics of the method

1. CONTAIN MEASURABLE VARIABLES

Data gathering instruments contain items that solicit measurable characteristics of the population. These measurable characteristics are referred to as the variables of the study, such as age, the number of children, educational status, and economic status.

2. USE STANDARDIZED RESEARCH INSTRUMENTS

The data collection instruments include questionnaires, polls, or surveys. Standardized, pre-tested instruments guide data collection, thus ensuring the accuracy, reliability and validity of data. Pre-testing helps identify areas in the research instruments that need revisions. It makes sure that respondents provide the expected answers or satisfy the intent of the researcher to meet the research objectives.

3. ASSUME A NORMAL POPULATION DISTRIBUTION

For more reliable data analysis of quantitative data, a normal population distribution curve is preferred over a non-normal distribution. This requires a large population, the numbers of which depend on how the characteristics of the population vary. This requires adherence to the principle of random sampling to avoid researcher bias in interpreting the results that defeat the purpose of research.

4. PRESENT DATA IN TABLES, GRAPHS, OR FIGURES

The data obtained using quantitative methods are organized using tables, graphs, or figures that merge large numbers of data to show trends, relationships, or differences among variables. This fosters understanding of the readers or clients of the research investigation.

5. USE REPEATABLE METHOD

Researchers can repeat the quantitative method to verify or confirm the findings in another setting. This reinforces the validity of groundbreaking discoveries or findings, thus eliminating the possibility of spurious or erroneous conclusions.

6. CAN PREDICT OUTCOMES

Quantitative models or formula derived from data analysis can predict outcomes. If-then scenarios can be constructed using complex mathematical computations with the aid of computers.

7. USE MEASURING DEVICES

Advanced digital or electronic instruments are used to measure or gather quantitative data from the field. The instruments ensure an objective and accurate collection of data provided that these are calibrated. Calibration means that the instruments used by the researcher matches the measurements of a reference instrument that is considered a standard.

The characteristics of quantitative research methods listed in this article make this research approach popular among researchers. Using qualitative research methods, however, is appropriate on issues or problems that need not require quantification or exploratory in nature.

MAIN USES OF THE METHOD

You can use quantitative research methods for descriptive, correlational, or experimental research.

• In descriptive research, you simply seek an overall summary of your study variables.
• In correlational research, you investigate relationships between your study variables.
• In experimental research, you systematically examine whether there is a cause-and-effect relationship between variables.
• Correlational and experimental research can both be used to formally test hypotheses, or predictions, using statistics. The results
• may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Advantages of the Quantitative Research

Quantitative research is often used to standardize data collection and generalize findings.

• Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, at times, or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection, and testing methods before coming to a conclusion.

Disadvantages of the Quantitative Research

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics.

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data, imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.