Statistical analysis in a research paper involves the examination and evaluation of numbers. Statistical analysis involves the application of statistical tools and formulas. It requires proper and appropriate usage while writing a research paper. Improper usage of statistical analysis leads to invalid results and wrong conclusions.
So, it is important to write statistical analysis in a research paper correctly. There are many ways to incorporate statistical analysis into a research paper. So, you must learn to use them properly. This article will tell you everything you need to know about writing statistical analyses in research papers.
What is Statistical analysis?
Statistical analysis refers to the examination and evaluation of quantitative data. It involves the identification of data patterns and relationships between the data points. It is a scientific tool used in quantitative research. Statistical examination is a common technique in research.
According to a Dissertation help firm, Researchers use it to prove or disprove a hypothesis. They do so by understanding the relationship between independent and dependent variables. It involves the application of statistical tools. One of the common statistical tools is SPSS (Statistical Package for Social Sciences).
What are the different ways to write statistical analysis in a research paper?
Develop a Hypothesis
The first step is to define a clear and testable hypothesis. A hypothesis is an educated guess. It makes an assumption about the tentative outcomes of a specific phenomenon. A researcher seeks to refute or validate the hypothesis based on gathered data. But stating a clear hypothesis is essential for writing statistical evaluation. A clear hypothesis has an independent variable and a dependent variable.
The researchers seek to identify the impact of the independent variable on a dependent variable. In some situations, such as experimental research, researchers often manipulate the independent variable to observe its impact on the dependent variable. So, it is important to write a testable hypothesis that the researcher can prove or disprove through data collection and statistical analysis. There are two types of hypotheses important to construct for statistical evaluation. Here are the two types of hypotheses:
A null hypothesis states no connection or relationship between the variables.
The alternative hypothesis states a connection or relationship between the variables.
Define the aims and goals
The second most important thing is to define the aims and objectives of statistical evaluation clearly. Statistical analysis involves experimental analysis, correlational analysis, and descriptive analysis. Experimental analysis requires setting up an experiment and observing the causality between an independent variable and a dependent variable. Correlation analysis entails identifying the relationship between the variables than the causality. Descriptive analysis involves the description of attributes and features of a statistical population.
Define the Sample Size
Statistical evaluation requires an appropriate sample size. A proper sample size helps in data collection and leads to reliable results. So, it is important to use some sampling techniques to define the sample size. You should define the parameters and the sampling criteria for choosing a particular population. The sample size should represent the entire population. It is because the findings of the statistical evaluation are generalisable.
You can use probability sampling and non-probability sampling techniques. The probability sampling method is a technique where the population sample is selected randomly. Non-probability sampling is convenience sampling. It means that a researcher selects a sample size based on convenience.
Start collecting the Data
After defining the sample size, you should start collecting the data. Make sure you are using appropriate data collection tools to gather reliable data.
Perform the Descriptive Statistics first
It is important to perform descriptive statistics first for writing statistical analysis. Descriptive statistics requires defining the features of the sample size. After collecting the data, store it in a central repository.
You can also use STATA and SPSS for data cleaning and organisation. The next step is to analyse the data and provide descriptive summary data using statistical tools. You can do the following things:
Data inspection pertains to the following things:
- Using frequency distribution tools to organise data from each variable
- Using bar charts and pivot tables to represent the data from each variable and to indicate the distribution of data points
- Using scatter plots to represent the connection between two variables visually.
Visual aid is important to incorporate in your research paper while writing statistical evaluation. It helps the readers understand the data and increases their comprehension. You can use tables and graphs to provide a striking representation of data and indicate central tendency measures. Also, visual representation is quite helpful for indicating a normal and skewed distribution of data.
Calculate Mean Median and Mode
The calculation of measures of central tendency is paramount while writing statistical analysis. Mode indicates the recurring value throughout the data. The Median is the middle value in the order of high to low. Mean is the average of all values you can obtain by adding all values and dividing them by the number of values.
Find Out the Measures of Variability
Measures of variability help you understand the dispersion of values throughout the data set. You can use range, interquartile range, standard deviation, and variance to measure the dispersion.
Perform Inferential Statistics
Inferential statistics involves drawing inferences from the data to validate or refute the hypothesis. You can generate valid results through statistical inferences. The inferences method pertains to examining the data patterns and deriving conclusions based on set parameters. You can use estimation statistics for drawing inferences.
Estimation involves point estimates and interval estimates. Also, you can perform regression analysis and correlational analysis to understand the relationship between variables.
Minimise Type 1 and Type 2 errors
Type 1 and Type 2 errors are decision errors that frequently occur in statistical evaluation. Type 1 error refers to rejecting the null hypothesis when it is true. On the other hand, a Type 2 error occurs when the researcher fails to reject the null hypothesis when it is false. You can avert these errors by maintaining a balance and choosing the appropriate significance level.
Writing a statistical analysis using different techniques is important for generating deep insights. It leads to accurate findings and helps researchers predict future outcomes and patterns. The statistical evaluation also requires defining the hypothesis clearly and indicating a clear relationship between variables.