Importance Of Quantitative Research PDF

\ ***TYPES OF QUANTITATIVE RESEARCH***\ A. ***Descriptive Research*** -- it seeks to describe the current status of an identified variable. These research projects are designed to provide systematic information about a phenomenon. The researcher does not usually begin with a hypothesis, but is likel...

\ ***TYPES OF QUANTITATIVE RESEARCH***\ A. ***Descriptive Research*** -- it seeks to describe the current status of an identified variable. These research projects are designed to provide systematic information about a phenomenon. The researcher does not usually begin with a hypothesis, but is likely to develop one after collecting data. The analysis and synthesis of the data provide the test of the hypothesis.\ Example given A description of how second-grade students spend their time during summer vacation.\ A description of the attitudes of scientists regarding global warming\ \ B. ***Correlational Research*** -- attempts to determine the extent of a relationship between two or more variables using statistical data. In this type of design, relationships between and among a number of facts are sought and interpreted. This type of research will recognize trends and patterns in data, but it does not go so far in its analysis to prove causes for these observed patterns. Cause-and-effect is not the basis of this type of observational research. The data, relationships, and distributions of variables are studied only. Variables are not manipulated; they are only identified and are studied as they occur in a natural setting.\ *Example given:* The relationship between intelligence and self-esteem\ The covariance of smoking and lung disease\ The relationship between diet and anxiety\ \ C. ***Causal-Comparative (quasi-experimental research)*** -- it attempts to establish cause - effect relationships among the variables. These types of design are very similar to true experiments, but with some key differences. An **independent variable** is identified but not manipulated by the experimenter, and effects of the independent variable on the **dependent variable** are measured. The researcher does not randomly assign groups and must use ones that are naturally formed or pre-existing groups. Identified control groups exposed to the treatment variable are studied and compared to groups who are not.\ *Example given:* The effect of part-time employment on the achievement of high school students.\ The effect of taking multivitamins on a students' school absenteeism\ The effect of gender on algebra achievement.\ \ D. ***Experimental Research*** -- it is often called true experimentation, uses the scientific method to establish the cause-effect relationship among a group of variables that make up a study. 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.\ *Example given:* The effect of a new treatment plan on breast cancer.\ The effect of positive reinforcement on attitude toward school\ \ The study of Madrigal and McClain as cited by Melegrito and Mendoza (2016), only those with a firm grasp on how the data would be used and interpreted should conduct such a study. This is observed when researchers over rely on the P value or calculated value, and sample size. The P Value or the probability of finding the observed or more extreme, results when the **null hypothesis (Ho)** of a study is true. The **null hypothesis** is usually a hypothesis of 'no difference' e.g. no significant difference between blood pressures of controlled group and experimental group. A statistically significant result cannot prove that a research hypothesis is correct (as this implies 100% certainty). Because a p-value is based on probabilities, there is always a chance of making an incorrect conclusion regarding accepting or rejecting the null hypothesis (Ho). *Two Types of Errors are inversely proportional: that is, decreasing type I error rate increases type II error rate, and vice versa (McLeod, 2019).\ ***A. A type 1 error is also known as a false positive** and occurs when a researcher incorrectly rejects a true null hypothesis. This means that your report that your findings are significant when in fact they have occurred by chance.\ **B. A type II error is also known as a false** **negative** and occurs when a researcher fails to reject a null hypothesis which is really false. Here a researcher concludes there is not a significant effect, when actually there really is.\ \