What is Quantitative Data Analysis?
Quantitative data analysis is the process of applying statistical techniques to transform and model numeric data to obtain useful insights and draw useful conclusions from it. It is typically used to measure relationships between variables, differences between groups, and test hypotheses. Quantitative data analysis entails analyzing numbers and involves the use of basic or more advanced statistical analyses to discover patterns in the data. The results are often reported in graphs and tables. Quantitative analysis provides quantifiable and easy-to-understand results compared to qualitative analysis. Nevertheless, there are many different statistical techniques used in analyzing quantitative data and things can get complicated, especially when you lack a solid understanding of the basics and analysis techniques that are specific to your research. It is also very easy to conduct an analysis that is simply wrong or inappropriate for your purposes and generate elegantly presented but flawed results. Hence, you may need to hire an expert statistician to guide you about the most relevant statistical techniques to use for your data. This has been made easier by many academic writing companies, such as Project Editing Help, which provide professional quantitative data analysis service online. You can also seek advice from your supervisor regarding the most appropriate statistical analyses for your research and the choice of statistical software.
Data Preparation in Quantitative Data Analysis
Before the analysis of any quantitative data, it is important to prepare the data. Data preparation includes three steps, namely; data validation, data screening & cleaning, and data coding.
Data validation: It entails checking whether the data was collected as per the pre-set standards. For instance, you can check the completeness of the data if it was gathered using survey questionnaires to ensure the respondents answered all the questions. Also, you can check the response rate to ensure the data collected is without any bias, such as participation (non-response) bias. You may need to re-administer the survey if you establish that there is some bias since the outcomes will be inaccurate and not representative of the population.
Data screening & cleaning: Qualitative data usually include errors. For instance, respondents may incorrectly fill some fields or accidentally skip them. To ensure there are no such errors or inconsistencies, it is vital to check your data set for missing data, incorrect or irrelevant values, duplicates, and outliers. All these data points may hamper the accuracy of the results and need to be removed or corrected.
Data coding: This step entails grouping and assigning values to the data to simplify the analysis. For instance, you can code data for gender by assigning the value of 1 to males and 2 to females. Most researchers use Excel sheets to prepare their data for the analysis which is then imported into the preferred statistical analysis software such as SPSS and R. Need help with data preparation or data cleansing and coding? Our experts will do it for you at affordable rates.
Quantitative Data Analysis Methods
The commonly used methods for analyzing quantitative data are descriptive statistics (or descriptive analysis) and inferential statistics (or inferential analysis). Descriptive statistics is the first level of analysis that helps in summarizing individual variables and finding patterns by providing absolute numbers. On the other hand, inferential statistics involve are complex analyses that are used to show relationships between multiple variables to generalize the results and make predictions. You might only need to use only descriptive statistics or a mix of both analyses in your research depending on the research aims, objectives, and questions.
a) Descriptive Statistics
Descriptive statistics are used to describe the data set and help to understand the details of the specific sample used in research, rather than making inferences about the entire population. They are the first set of statistics covered while writing up the analysis chapter before moving on to inferential statistics. However, they may be the only method of analysis used depending on your research aims, objectives, and questions. The following are some of the commonly used descriptive statistics;
Mean: It is the numerical average of a set of values. It is a popular measure of central tendency, especially when there are no outliers in a data set.
Median: It is the midpoint of a set of values; a useful measure of central tendency when a data set has outliers and the values distribute very unevenly.
Mode: It is the most common value or that which is observed most often in a set of values. It is a useful measure of central tendency when one is interested in finding the most popular value.
Frequency distribution: It is the number of times a value is observed in a data set. It is usually expressed in percentage and shows how frequently the specific values are observed.
Range: It is the difference between the maximum value and minimum value in a data set. It is a measure of dispersion.
Standard deviation: It is a measure of how values in a data set are spread out. It shows how much the value exits from the mean.
b) Inferential Statistics
Inferential statistics are used to make inferences about the population and to make predictions. They are useful when you are interested in investigating the differences between groups, relationships between variables, and hypothesis testing. Common inferential statistical techniques include t-tests, ANOVA, correlations, and regressions. The inferential statistics to use in your analysis depends on the type of data and the skewness of the data (symmetry and normality). Parametric tests are used only when a normal distribution of values is assumed. The most commonly used parametric tests are; t-tests, ANOVA, linear regression, and Pearson rank correlation. On the other hand, non-parametric tests are used in cases where parametric tests are not appropriate. Common scenarios when non-parametric tests are used are when the data is not normally distributed (skewed distribution), distribution is not known, the size of the sample is too small (n<30) to assume a normal distribution, or when dealing with discrete and categorical (nominal or ordinal) variables. The most widely used tests are; Chi-square, Fisher’s exact tests, Wilcoxon’s matched pairs, Mann-Whitney U-tests, Kruskal-Wallis tests, and Spearman rank correlation. Read more on selecting parametric and non-parametric tests.
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