This Week You Learned About The Three Major Research Methods
This Weekyou Learned About The Three Major Research Methods Available
This week you learned about the three major research methods available to researchers in the counseling profession: quantitative, qualitative, and mixed methods. When thinking about quantitative research designs and collecting data, researchers use descriptive statistics, parametric statistics, and non-parametric statistics. It is important to understand the types of data that would be appropriate for use with parametric and non-parametric statistics when engaging in quantitative research. Review Scales of Measurement. Then, for this discussion: List each type of data used in quantitative research. Provide an example for each type of data. Identify the type of quantitative data that can be used with each form of statistics (parametric and non-parametric).
Paper For Above instruction
Quantitative research encompasses various data types that serve as the foundation for conducting statistical analyses. Understanding these data types, along with their appropriate measurement scales, is crucial for selecting suitable statistical methods—parametric or non-parametric—and ensuring valid research outcomes.
The primary types of data used in quantitative research include nominal, ordinal, interval, and ratio data. Each serves specific purposes within the measurement of variables, and each has appropriate applications depending on the statistical analysis being performed.
Nominal Data
Nominal data represent categories or labels without any inherent order or ranking. They are used to classify objects or individuals into distinct groups. An example of nominal data would be the classification of participants by gender: male, female, or other. Since nominal data do not possess a meaningful order, they are best analyzed using non-parametric statistical methods such as chi-square tests, which assess differences in categorical distributions.
Ordinal Data
Ordinal data involve categories with a clear, meaningful order but without consistent intervals between categories. An example would be a satisfaction survey rated on a scale: dissatisfied, neutral, satisfied, very satisfied. Ordinates can be analyzed with non-parametric methods such as Mann-Whitney U or Kruskal-Wallis tests, which compare medians across groups.
Interval Data
Interval data have ordered categories with equal spacing between values but lack a true zero point. An example includes temperature measured in Celsius or Fahrenheit. Since the intervals are equal, interval data are suitable for parametric tests like t-tests or ANOVA, provided the data meet assumptions such as normality and homogeneity of variances.
Ratio Data
Ratio data possess all the properties of interval data, with the addition of a meaningful zero point, which allows for the calculation of ratios. An example would be the number of hours spent studying. Ratio data are highly versatile and can be analyzed using both parametric and non-parametric methods, depending on the distribution. When data are normally distributed, parametric tests like ANOVA or regression are appropriate; otherwise, non-parametric tests such as the Mann-Whitney U test may be more suitable.
In conclusion, understanding the different types of data—nominal, ordinal, interval, and ratio—and their appropriate statistical analyses is vital for researchers employing quantitative methods in counseling. Proper alignment of data types with statistical tests ensures accurate, reliable insights, guiding evidence-based practice effectively.
References
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