Further Quantitative Research Method (Comprehensive)- ver.1

0. Basic Statistical Approach

ㅡ Descriptive Statistics:

Descriptive Statistics involve summarizing and describing data through measures such as central tendency (mean, median, mode) and va2riability (standard deviation, range). Descriptive statistics provide a basic understanding of the data distribution and key characteristics.

ㅡ Inferential Statistics:

Inferential statistics are used to make inferences and draw conclusions about a population based on a sample. These methods include hypothesis testing, confidence intervals, and determining statistical significance. Inferential statistics help researchers generalize findings and make statements about the broader population.

ㅡ Sampling Techniques:

Sampling techniques are used to select representative samples from larger populations. These methods include simple random sampling, stratified sampling, and cluster sampling. Proper sampling techniques ensure that research findings can be generalized to the population of interest.

1. Experimental Design:

Experimental design involves manipulating independent variables and measuring their effects on dependent variables while controlling for conExperimental Design: Experimental design involves manipulating independent variables and measuring their effects on dependent variables while controlling for confounding factors. Researchers randomly assign participants to different conditions and use statistical analysis to determine the impact of the independent variable on the outcome.

2. Survey Research:

Surveys are widely used in quantitative analysis to gather data from a large sample of individuals. Researchers develop structured questionnaires with closed-ended questions, and participants provide responses on a rating scale or by selecting predefined options. Surveys allow for the collection of numerical data that can be analyzed quantitatively.

3. Longitudinal Study:

Longitudinal studies involve collecting data from the same participants over an extended period. This method allows researchers to examine changes and patterns of behavior or phenomena over time. Longitudinal studies can involve repeated measurements, tracking trends, and analyzing correlations or changes across different time points

4. Correlational Research:

Correlational research focuses on exploring relationships between variables without manipulating them. Researchers collect data on multiple variables and analyze the strength and direction of their associations using statistical techniques such as correlation analysis. Correlational studies help identify patterns and associations between variables of interest.

5. Meta-analysis:

Meta-analysis is a statistical technique that combines and analyzes data from multiple independent studies to draw generalizable conclusions. It involves synthesizing and integrating findings from different studies to provide a more comprehensive and reliable estimate of the overall effect size or relationship between variables.

6. Secondary Data Analysis:

In quantitative research, researchers often analyze existing datasets that were collected for other purposes. Secondary data analysis involves utilizing publicly available datasets or accessing data from previous studies to answer new research questions. This method can save time and resources while still allowing researchers to conduct rigorous quantitative analysis.

7. Statistical Modeling:

Statistical modeling involves using mathematical and statistical techniques to develop models that represent relationships between variables. This can include regression analysis, structural equation modeling, or multilevel modeling. Statistical models allow researchers to make predictions, test hypotheses, and explore complex relationships within datasets.foundinExperimental Design: Experimental design involves manipulating independenExperimental Design: Experimental design involves manipulating independent variables and measuring their effects on dependent variables while controlling for confounding factors. Researchers randomly assign participants to different conditions and use statistical analysis to determine the impact of the independent variable on the outcome.

8. Meta-analysis:

Meta-analysis is a statistical technique that combines and analyzes data from multiple independent studies to draw generalizable conclusions. It involves synthesizing and integrating findings from different studies to provide a more comprehensive and reliable estimate of the overall effect size or relationship between variables.

9. Secondary Data Analysis:

In quantitative research, researchers often analyze existing datasets that were collected for other purposes. Secondary data analysis involves utilizing publicly available datasets or accessing data from previous studies to answer new research questions. This method can save time and resources while still allowing researchers to conduct rigorous quantitative analysis.

10. Analysis of Variance (ANOVA):

ANOVA is a statistical technique used to compare means across multiple groups or conditions. It assesses whether there are statistically significant differences among the means and helps identify which groups are significantly different from each other. ANOVA can be used with different designs, including one-way ANOVA for comparing groups on a single factor and factorial ANOVA for examining interactions between multiple factors.

11. Structural Equation Modeling (SEM):

SEM is a statistical method that combines factor analysis and path analysis to test and validate complex theoretical models. It allows researchers to examine the relationships between observed variables, latent variables, and error terms. SEM is often used to assess model fit, test hypothesized relationships, and explore mediating and moderating effects.

12. Survival Analysis:

Survival analysis, also known as event history analysis, is used to analyze time-to-event data, such as time to failure or time to an event occurrence. It takes into account censoring, where some individuals do not experience the event of interest within the study period. Survival analysis uses techniques such as Kaplan-Meier estimation and Cox proportional hazards regression to model and analyze survival data.

13. Discriminant Analysis:

Discriminant analysis is a statistical technique used to classify cases into pre-defined groups based on a set of predictor variables. It helps determine which variables contribute the most to group separation and can be used for prediction or classification purposes. Discriminant analysis is often employed in fields such as marketing, social sciences, and psychology.

14. Item Response Theory (IRT):

IRT is a statistical modeling approach used to analyze the characteristics and properties of items in psychometric tests or questionnaires. It focuses on the relationship between individuals' responses and the latent traits being measured. IRT allows for the estimation of item difficulty, discrimination, and individual ability levels. It is commonly used in educational and psychological measurement.

15. Time Series Analysis:

Time series analysis is a method for analyzing data collected over time to identify patterns, trends, and relationships. It involves examining the temporal dependencies and characteristics of the data, such as seasonality, trends, autocorrelation, or forecasting future values. Time series analysis utilizes techniques like autoregressive integrated moving average (ARIMA), exponential smoothing, or state space models.

16. Ex post facto Research:

Ex post facto research involves studying the relationship between variables after the fact, without manipulation or random assignment. Researchers identify pre-existing groups or conditions and examine their potential effects on the outcome of interest. This method is often used when experimental manipulation is not feasible or ethical.

17. Quasi-Experimental Design:

Quasi-experimental designs resemble experimental designs but lack random assignment to conditions. Researchers select groups that naturally differ on the independent variable and compare their outcomes. Quasi-experiments allow for some control over confounding factors while acknowledging limitations in establishing causality.

18. Factor Analysis:

Factor analysis is a statistical method used to identify underlying latent factors or dimensions within a set of observed variables. It helps researchers understand the structure or patterns of relationships among variables. Factor analysis can be exploratory, where factors emerge from the data, or confirmatory, where researchers test pre-defined factor structures.

19. Cluster Analysis:

Cluster analysis is a technique used to classify objects or cases into groups, or clusters, based on similarities or dissimilarities among variables. It helps researchers identify patterns, subgroups, or typologies within their data. Cluster analysis can be useful for segmentation or identifying distinct profiles within a population.

20. Network Analysis:

Network analysis explores the relationships or connections among entities within a system. It is used to study social networks, organizational structures, or complex relationships between variables. Network analysis involves measuring ties, identifying central nodes, and analyzing network properties using mathematical models and visualization techniques.

21. Econometric Analysis:

Econometric analysis applies statistical techniques to economic data to estimate and test economic theories and models. It involves analyzing relationships between economic variables, such as supply and demand, using econometric models and regression analysis. Econometrics often employs time series analysis and panel data techniques.

22. Cluster Analysis:

Cluster analysis is a technique used to group similar cases or individuals together based on their characteristics or variables. It aims to identify distinct clusters or subgroups within a larger dataset. Cluster analysis can be helpful in identifying market segments, customer profiles, or typologies based on similarities and differences between cases.

23. Principal Component Analysis (PCA):

PCA is a dimensionality reduction technique used to identify patterns and underlying factors within a dataset. It transforms a set of correlated variables into a smaller set of uncorrelated variables, known as principal components. PCA can help simplify complex datasets, identify key variables, and reduce multicollinearity in subsequent analyses.

24. Meta-analysis:

Meta-analysis is a statistical technique that combines results from multiple independent studies to draw conclusions and establish more robust findings. It involves systematically collecting, evaluating, and synthesizing data from different studies to estimate the overall effect size or relationship between variables. Meta-analysis can provide a comprehensive overview of the existing evidence on a specific research topic.

25. Conjoint Analysis:

Conjoint analysis is a quantitative research method used to measure and understand how people make choices or decisions. It examines the preferences and trade-offs individuals make when presented with different product or service attributes. Conjoint analysis helps researchers understand the relative importance of attributes and predict consumer preferences.

26. Growth Curve Analysis:

Growth curve analysis is used to model and analyze repeated measurements or observations of individuals over time. It allows researchers to examine individual trajectories of change, estimate growth parameters, and assess differences in growth patterns between groups. Growth curve analysis is often applied in longitudinal studies or developmental research.

27. Data Envelopment Analysis (DEA):

DEA is a nonparametric method used to assess the relative efficiency and performance of multiple decision-making units, such as companies, organizations, or regions. It compares the inputs used and outputs produced by different units to determine their efficiency levels and identify benchmarks for improvement.

28. Parametric Tests:

Parametric tests assume specific distributional characteristics of the data, such as normality. They include t-tests for comparing means between two groups, analysis of variance (ANOVA) for comparing means among multiple groups, and linear regression for examining relationships between variables.

29. Nonparametric Tests:

Nonparametric tests are used when assumptions of parametric tests are not met or when working with data on an ordinal or nominal scale. Examples include the Mann-Whitney U test, Kruskal-Wallis test, and chi-square test. Nonparametric tests provide distribution-free alternatives for statistical analysis.

30. Power Analysis:

Power analysis is conducted to determine the required sample size for a study to detect a meaningful effect or relationship. It helps researchers estimate the statistical power of their study and determine the appropriate sample size to achieve desired levels of power.

31. Psychometrics:

Psychometrics focuses on the measurement of psychological constructs and the development of reliable and valid measurement instruments. This includes techniques such as item analysis, reliability analysis (e.g., Cronbach's alpha), and validity assessment (e.g., construct validity, criterion validity).