How do different statistical methods affect the results of correlational research?
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Different statistical methods impact correlational research differently. For instance, in rehabilitation science, using Mixed Models for correlated data is preferred due to its flexibility in modeling correlation structures. In the context of detecting differences between correlations, Fisher's method and linear mixed-effects models were found to be less effective compared to Cohen's method and an ad hoc developed procedure integrating bootstrap and effect size estimation. Additionally, when assessing scale-dependent correlations, Detrended Cross-Correlation Analysis (DCCA) methods were shown to be more robust and accurate compared to traditional Pearson correlation methods. Moreover, in a study on atherosclerosis, resampling data at lower resolution and utilizing Fourier or wavelet transforms helped mitigate spatial autocorrelation issues, providing more accurate results in correlational analysis.
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| Papers (5) | Insight |
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35 Citations | Detrended Cross-Correlation Analysis (DCCA) quantifies scale-dependent correlations robustly, while Pearson method is sensitive to noise and amplitude ratio variations, impacting correlational research outcomes. |
| Different statistical methods in correlational research can lead to substantial differences in results due to spatial autocorrelation; using Fourier or wavelet transforms and bootstrapping can mitigate these discrepancies. | |
| Different statistical methods, such as doubly-weighted estimators and marginal structural Cox models, can improve efficiency and accuracy in analyzing correlated data from observational studies, as shown in the research. | |
5 Citations | Different statistical methods like Summary measures, Repeated Measurement ANOVA, MANOVA, and Mixed Models impact correlational research results. Mixed Models are preferred for their flexibility in modeling correlation structures. |
1 Citations | Different statistical methods like Cohen's q, Fisher's method, LMEM, and bootstrap with effect size estimation impact correlational research results by varying in sensitivity to data structures and sample sizes. |
Related Questions
What does the correlational research methodologies determine?5 answersCorrelational research methodologies determine the relationship and degree of association between two or more variables, aiding in hypothesis testing and theory strengthening. They are crucial for identifying correlations between semi-personalisation functionalities and customisation settings in Mobility as a Service (MaaS) applications, guiding the development of personalized services. Additionally, correlational analysis using the Principal Component Analysis (PCA) method helps in understanding the impact of socioeconomic variables on academic performance in Architecture students. Researchers commonly use correlation and regression analyses to showcase connections between factors in datasets, essential for statistical analysis. The Pearson Product Moment Coefficient is a common tool for investigating the direction and strength of relationships between variables, highlighting the importance of not inferring causation from correlation.
Why correlational study for research?4 answersCorrelational studies in research are crucial as they help establish relationships between different variables without manipulating them, providing valuable insights into how changes in one variable may be associated with changes in another. These studies are essential in various fields like masonry material properties assessment, dental research data analysis, and image registration accuracy enhancement. By recognizing and accounting for correlations in data, researchers can avoid erroneous conclusions and ensure the validity of their results. Correlational research allows for a deeper understanding of complex phenomena by identifying patterns and connections between variables, ultimately contributing to the advancement of knowledge and the development of more accurate models and predictions.
How correlational research is use in a study?4 answersCorrelational research is used in a study to determine the prevalence and relationships among variables, as well as to forecast events based on current data and knowledge. It is widely used in various fields, including nursing and health research, to inform decision-making and improve or initiate health-related activities or change. In addition, correlational research can be used to indirectly derive elastic and toughness properties of materials, minimizing the need for extensive experimental studies. It can also be applied in the analysis of mobile network user behavior to identify correlations between different aspects such as district, time, and type of business. Furthermore, correlational research can be used to analyze the relationship between the number of attendees at conferences and various Twitter metrics, providing insights into the impact of social media on professional events.
What is correlational research?5 answersCorrelational research is a type of research that focuses on determining the relationship and degree of relationship between two or more variables. It involves collecting data and analyzing the existing relationships between variables. This type of research is important for hypothesis testing, theory strengthening, or rejecting previously studied hypotheses. Correlational research is often used in quantitative research with a descriptive approach, where the primary area of interest is not manipulated by the researcher. Surveys and observational methods are commonly used to gather data for correlational research. The goal of correlational research is to understand how things are related and provide explanations based on the research objectives.
What to use in analyzing data in correlational studies?5 answersIn correlational studies, researchers use correlation and regression techniques to analyze data and identify relationships between variables. Correlation analysis is particularly useful in exploring the associations between independent and dependent variables. It helps in understanding the level of multicollinearity and the mediating/moderating status of independent variables in a model. Additionally, correlation analysis is significant for exploring complex interactions and relationships among variables in multivariate data sets. It helps researchers gain an in-depth understanding of the data by identifying salient scalars and analyzing the correlations among variables. Techniques such as covariance, cross-correlation, and coherence are used to quantify correlations within and among time series data.
What are the different types of correlation research designs?5 answersCorrelation research designs can be categorized into different types. One type is the mixed effects model with crossed random effects, which considers the sampling of both participants and stimuli. Another type is the Correlation Coordinate Plots (CCPs), which transform data into a coordinate system for estimating the direction and strength of correlation. There is also the strongly directionally equineighboured (SDEN) design, which has a constant multiple of the variance matrix of the generalized least-squares estimates of treatment effects under general within block correlation structure. Additionally, correlational research designs are used to test relationships between or among variables of interest, as described in the contemporary vocational rehabilitation literature. Finally, there are alternative approaches to the traditional Pearson's R correlation coefficient, such as using the mean absolute deviation as a measure of dispersion.