Statistical Tests One Should Know Before Writing a Doctoral Dissertation
To develop a successful dissertation, one must carefully plan for the process, starting from specifying the hypothesis or developing the research questions to analyzing and interpreting the data. Statistical analysis of dissertation data is complex, requiring advanced analytical skills and a deep understanding of research designs and the appropriate test methods, which can be overwhelming to some scholars. As such, one can hire a statistician to help navigate the dissertation data analysisprocess with confidence. Our experienced statisticians possess the advanced skills required for data analysis and have access to the most advanced statistical software to assist clients with their statistical tests.
In this article, we have discussed the 10 common statistical tests used in doctoral dissertations, including their purpose, applications, and examples.
Key Statistical Tests Explained: Insights From a Statistician for Hire
1. T-Tests
A t-test is a statistical test to compare the means of two populations to check whether they are statistically different. There are two types of t-tests: The independent samples t-test, which is used when comparing two different groups, and the paired samples t-test, which is used when measuring the same group under two different conditions or at two different points. T-tests are commonly used in dissertation research, particularly in experimental and quasi-experimental studies, when the researcher seeks to establish the outcome of an intervention or treatment on a single variable.
Example
In a psychology dissertation, a researcher aims to evaluate the impact of a mindfulness program on stress levels in graduate students. A paired t-test would help compare pre- and post-staffing scores in the individuals. A t-test calculates a t-value or t-statistic based on the standard deviations, sample sizes, and means of the two groups. The assumption of a t-test is that the data are normally distributed and measured on a ratio or interval scale.
2. ANOVA
Analysis of variance (ANOVA) is a statistical technique used when the study involves a comparison of more than two groups. Researchers and statisticians use ANOVA to determine the existence of statistically significant differences among the means of three or more independent groups. The ANOVA test is primarily used in dissertations involving experimental designs with multiple demographic categories or treatment conditions.
Example
Suppose a scholar conducts a doctoral dissertation in public health to compare the efficacy of three different diet programs in lowering cholesterol levels. Using ANOVA, the researcher tests the existence of a significant difference in the mean reduction of cholesterol levels in these programs. However, through ANOVA, one only knows that at least one group is different; ANOVA does not indicate which one. Post-hoc tests, such as Tukey’s HSD, are needed to determine where the difference occurs. To properly select, interpret, and perform ANOVA tests, one can seek help from our statistician for hire, who guarantees methodological rigor and accurate results.
3. F-Test
The variance test (F-test) is a key test used in most statistical procedures, including ANOVA and regression. Researchers use the F-test to evaluate the equality of variance of two or more populations and assess the significance of models explaining differences in the data. The F-test establishes whether the overall regression procedure is significant because it compares the sum of the unexplained and explained variance. In doctoral dissertation research, the F-test is useful in comparing models and testing the applicability of nested models in hypothesis-driven studies.
Example
A researcher can conduct a business dissertation to compare the financial risk of two investment strategies; an F-test can be done to check whether variances in the returns differ significantly. The F-test is sensitive to normality assumption and is commonly employed as a preliminary to other analyses. The F-test serves as the statistical foundation for complex statistical tests, including MANOVA and regression.
4. MANOVA
Multivariate analysis of variance (MANOVA) enables researchers to examine multiple dependent variables simultaneously across groups. MANOVA test is most useful when the outcomes are correlated, and one has to control for increasing Type I errors. MANOVA is useful in dissertations that involve testing complex constructs, such as cognitive and behavioral outcomes, measured as a block across varied conditions of an experiment.
Example
Suppose a researcher conducts an educational dissertation to examine the effect of teaching style on math and reading scores. In that case, the researcher can use MANOVA to simultaneously test the significance of teaching style on student’s performance in both subjects. MANOVA assumes homogeneity of variance-covariance matrices and multivariate normality. Violating MANOVA assumptions can result in inaccurate conclusions; therefore, it is advisable to partner with statisticians for hire to ensure that the test is suitable for the analysis.
5. Regression Analysis
Regression analysis enables researchers to investigate the relationship between a dependent variable and one or more independent variables. Regression analysis exists in two common forms: Simple linear and multiple regression. The key difference between simple and multiple regression is that the latter involves two or more predictor variables, whereas the former uses only one.
Example
Suppose a researcher conducts a social science dissertation to investigate how work-life balance, income level, and job satisfaction predict overall well-being. In that case, regression helps identify the variables that significantly predict the outcome and the magnitude of their effect. The assumptions of regression models include linearity, homoscedasticity, and independence of observations. Most researchers find it challenging to diagnose and remedy problems such as multicollinearity or autocorrelation, which undermine the validity of the findings. Therefore, hiring our experts can help to ensure that the doctoral dissertation is accurate and submitted on time.
6. Chi-Square Test
The chi-square test is most suitable for analyzing the association between categorical variables. Two common forms are the chi-square test of independence, used to assess the relationship between two variables, and the goodness-of-fit test, which is used when comparing observed data to expected frequencies. Researchers commonly use chi-square tests when their dissertation studies involve survey data and when they are interested in testing relationships between demographic variables and categorical response data. The chi-square test is nonparametric, which makes it appropriate for data that fail to meet the assumptions of parametric alternatives.
Example
In a marketing dissertation, the researcher could investigate whether gender is linked to a preference for a new product. The chi-square test could be used to assess whether the two categorical variables are associated in a statistically significant manner. A limitation worth noting is that the test can be unreliable when small expected frequencies occur in the contingency tables; in such cases, exact tests or corrections may be necessary.
7. Mann – Whitney U Test
Also known as the Wilcoxon Rank-Sum Test, the Mann-Whitney U test is a nonparametric equivalent of the independent samples t-test employed when the data fails to comply with the normality assumptions. Mann-Whitney U Test compares the distributions of two independent samples of ranked data. The Wilcoxon Rank-Sum test is widely used in dissertations with small sample sizes or ordinal data, where parametric tests are unsuitable. Mann-Whitney U Test is particularly helpful in medical or behavioral studies where the outcomes of interest are pain ratings, satisfaction ratings, or symptom severity in treatment versus control groups.
Example
The Mann-Whitney U test is ideal for a dissertation where the researcher compares patients’ recovery rates under two rehabilitation programs, using recovery scores obtained on an ordinal scale. Mann-Whitney U test does not assume normal distribution and is effective in studies involving ordinal data or small samples. For those in need of help with the Mann-Whitney U test, we recommend seeking assistance from our expert statisticians today for analysis services delivered on time without compromising on time.
8. Wilcoxon Signed-Rank Test
The Wilcoxon signed-rank test is the nonparametric alternative to the paired t-test.The Wilcoxon Signed-Rank Test is employed when the researcher is comparing repeated measurements on one sample or two related samples, particularly when paired t-test assumptions are violated. The Wilcoxon Signed-Rank test is commonly used in dissertation research that employs pre-test and post-test designs, particularly when the data are ordinal, or the sample size is small.
Example
In an environmental science doctoral dissertation, the researcher may evaluate water quality variation before and after the implementation of a new conservation policy. When the measurements are not normally distributed, the Wilcoxon signed-rank test would be the test of choice. The Wilcoxon signed-rank test compares and ranks the differences between paired observations, making it resistant to outliers and skewed data.
9. Pearson Correlation
Pearson’s correlation test measures the strength and direction of the linear association between two continuous variables. Pearson correlation varies between -1 and 1, where 0 represents the absence of a linear relationship. The Pearson correlation test is commonly used in dissertations that aim to investigate relationships among key quantitative variables, such as the correlation between academic motivation and grade point average (GPA). The significance of the Pearson correlation can be used to support or refute theoretical assumptions and is, therefore, crucial in hypothesis-driven studies.
Example
In a clinical psychology dissertation, a researcher could investigate the relationship between the number of hours of physical activity (PA) per week and self-reported levels of anxiety. A Pearson correlation test would be used to determine the direction and strength of the relationship between the number of hours of PA per week and self-reported anxiety measures. However, the test assumes homoscedasticity, linearity, and a normal distribution of variables. Pearson correlation is easily misinterpreted, and researchers should exercise caution when conducting this test, as it does not imply causation. For accurate interpretation and prevention of errors, it is advisable to hire expert statisticians who can assist with correlation analyses, their appropriate use, and reporting.
10. Spearman Rank Correlation
Researchers use the Spearman rank correlation test when the variables are ordinal or when the data do not meet the assumptions of the Pearson correlation. Spearman rank correlation measures the extent to which the relationship between variables is monotonic and its strength using ranks. The Spearman rank correlation test is employed in cases where the dissertation involves Likert-scaled variables such as attitude, perception, or satisfaction scores. Spearman correlation would also be appropriate for small sample sizes or skewed data distribution, which can result in misleading parametric tests.
Example
In a sociology dissertation, one may examine how self-rated social status is linked to happiness levels. When the variables are ordinal, or the data do not follow a normal distribution, Spearman correlation is an ideal alternative to Pearson correlation. Spearman’s rank correlation is also useful when the relationship between variables is non-linear but consistently decreasing or increasing.
Why hire a statistician for a dissertation?
Comprehending statistical tests in theory is useful, but applying them accurately in a dissertation is challenging. Among the most frequent causes of dissertation rejections, revisions, or delays is related to statistical errors. One should partner with an expert to help avoid dissertation rejections, revisions, or delays. By hiring our statisticians for doctoral dissertation analysis, you can be assured of proper assumption testing, accurate model selection, and clear interpretation of the quantitative and qualitative analysis results.
Our professionals have the expertise and technical skills required to operate statistical software such as SPSS, R, STATA, SAS, Python, MATLAB, Minitab, or Excel to analyze data and interpret simple and complex data sets to help make informed conclusions. We customize our services based on our client’s requirements and deliver analysis findings according to their urgency, whether it involves a short or long turnaround time, without compromising on quality.
We also assist our clients in writing other sections of their dissertation, such as the methodology, discussion, and conclusion sections. Dissertation committees demand clarity, accuracy, and appropriate statistical justification. By purchasing our services, our clients are guaranteed acceptance of their dissertations by their universities for graduation.
Summary
Statistical analysis is among the most technical aspects of the doctoral dissertation writing journey. Whether building regression models or running t-tests, appropriate statistical analysis reinforces researchers’ arguments, supports conclusions, and enhance academic credibility. From the simple t-test to the advanced methods such as MANOVA and regression, these techniques are indispensable in providing accurate and clear answers to research questions. Contact us today to hire a dissertation statistician for the best quantitative data analysis services and save yourself the hassle of struggling with complex formulas, software, and statistical jargon. Also, you will save your time to concentrate on other academic activities.