Quantitative research

• Quantitative research is an objective, systematic, and empirical investigation of observable phenomena through the use of computational techniques.
• One purpose of quantitative research is to measure magnitude.
• The format of quantitative research is a pre-determined response category
• quantitative research has a wide breadth of data from large statistically representative sample.
• The time dimension of quantitative research is rapid.
• quantitative research utilizes probability sampling
• quantitative research uses structured techniques such as questionnaires as data collection tools.
• quantitative research produces results that generalize, compare, and summarize \.
• Methods or procedures of data gathering include items like age, gender, educational status, among others, that call for measurable characteristics of the population.
• A large population yields more reliable data, but principles of random sampling must be strictly followed to prevent researcher's bias.
• Quantitative research puts emphasis on proof, rather than discovery.
• Constructed theories are tested and validated based on how and why a phenomena occur.
• Quantitative research methodology usually requires large samples and is more expensive than qualitative research
• The produced knowledge from quantitative research could be so abstract and general for direct application to specific local situations, context, and individuals.s
• Research Design refers to the overall strategy that a researcher chooses in order to integrate the different components of the study in a coherent and logical way, thereby ensuring that the research problem is effectively addressed.
• quantitative research designs are mainly classified into: 1.)descriptive research, 2.) correlational research, 3.) comparative research, and 4.) experimental research.
• Descriptive Research - aims to describe or explain something about a particular phenomenon by collecting data through observation, interviews, questionnaires, etc.
• Correlational / Associational Research - aims to determine whether there is any relationship between two variables (independent variable and dependent variable).
• Comparative Research - aims to compare two groups with respect to one or several characteristics.
• Experimental Research - the investigator probes into the cause of an effect by exposing one or more experimental groups to one or more treatments or conditions. an independent variable is manipulated to determine the effects on the dependent variable.
• Comparative Research - aims to compare two groups on one or several characteristics.
• Inferential Statistics - used when we want to make conclusions based on sample results rather than the entire population.
• Descriptive-Comparative Research aims to describe and compare different groups or variables without implying causation. The researchers consider at least two entities and establishes a formal procedure for obtaining criterion data on the basis of which he can compare.
• Causal-Comparative Research - the investigator attempts to determine the cause or consequences of differences that already exist between or among groups of individuals; hence, it is also called "ex post facto" (after the fact) research.
• True Experimental Research - It is characterized by the random assignment of participants to experimental and control groups, allowing researchers to establish causal relationships.
• Quasi-Experimental Research - this differs from true experiments in the randomization of the subjects-subjects are not randomly assigned to control and experimental groups.
• Independent Variable - it is the stimulus or cause variable chosen by the researcher to determine the relationship of an observed phenomenon.
• Dependent Variable - it is the response variable or effect that is observed or measured to determine the effect of the independent variable. It changes when the independent variable varies.
• Intervening Variable - It is a variable whose existence is inferred but cannot be manipulated or controlled. It is a hypothetical variable used to explain causal links between other variable.
• Moderator Variable - it is a variable that influences the strength or direction of the relationship between an independent variable and dependent variable.
• Confounding Variable - It is an extraneous variable that is not actually measured or observed in the study. Unlike moderator variables, these types of variables are not intentionally studied but can unintentionally impact the outcomes of an experiment or study.
• Categorical Data - a type of data where the objects being studied are grouped into categories based on some qualitative trait. The resulting data are merely labels or categories.
• Under Categorical Data there are Nominal and Ordinal Data.
• Nominal Data represent categories that cannot be ordered in any particular way.
• Ordinal Data - represent categories that can be ordered from greatest to smallest, vice versa.
• Binary Data - a type of categorical data wherein there are only two categories. This type of data can either be nominal or ordinal.

  ex. Attendance, smoking status, medical test.
• Numerical Data - The objects being studied are "measured" based on some quantitative trait. The resulting data are set of numbers.
• Under Numerical Data there are Discrete Data, Interval Data, and Ratio Data.
• Discrete Data - can only take on certain values. This type of data can't be measured but can be counted.
• Interval Data - ordered, constant scale, but no natural zero; differences make sense, but ratios do not.