Mental health data analysis is essential for any research project that needs to make quantitative conclusions. After a period of carefully choosing a research focus, selecting a sample, planning methodologies, preparing a proposal, and collecting data, statistical analysis is the next hurdle to overcome.
However, many researchers find statistics baffling, often due to issues like jargon, complex formulae and software programs, and abstract math. This often makes statistical services for psychology research necessary.
As professionals in mental health outcomes analysis, we can help anyone from students to clinicians, academics, and professional organisations achieve their research goals. On this page, we’ll explore how, starting with an explanation of statistical methods in psychology, before discussing the benefits of professional data analysis support.
Once a research study is complete, researchers need to pull their observations and data findings together to come to a conclusion. In psychology, this typically means interpreting information about people’s behaviours and thought processes. This is done in one of two ways: descriptive or inferential statistics. Let’s take a closer look at what these are to determine when their use is appropriate.
Researchers use descriptive statistics when they want to organise or summarise their data set. This gives them a brief informational coefficient, which is either representative of an entire population or a sample of a population.1
Descriptive stats are usually divided into two categories: measures of central tendency and measures of variability. Measures of central tendency involve describing the typical, average, and center of a set of scores. For instance, the mode is the score that occurs most often in a distribution. Further, the median is the midpoint in the set, while the mean is the average of the distribution.
Measures of variability look at the spread in the distribution – how dispersed they are. Simply put, this form of statistics considers whether the data is similar and close to the mean, or if it demonstrates a lot of variability. For instance, the range shows how much of a difference there is between the highest and lowest scores in a data set. Standard deviation describes how far data points are from the average score, and variance is the square root of the standard deviation. Measures of variability can also detect whether there is any skewness in a data set, identifying outliers and guiding theoretical approaches.
Inferential statistics are used when researchers want to draw a conclusion about the population they studied based on their collected data. This information allows the researchers to make educated guesses or predictions about this group, or to infer generalisations about a larger one.
Inferential statistics are important as they show whether an effect is statistically significant or due to chance. Specifically, scores that have less than 5% chance of being due to error are usually considered significant. This is expressed with a p-value. However, mistakes can often be made in this stage, in the form of Type I and Type II errors. This is when researchers either make a false positive and conclude that results are significant when they’re not, or they miss detecting a potentially significant finding.
Inferential statistics are often required when data is complex and multi-dimensional, as differential statistics may be too simplistic. For this reason, inferential statistics can require intermediate to expert knowledge of tests, which brings us to advanced statistics.
Oftentimes, a research question can be answered through simple statistical methods. For example, if someone wanted to see if there was a relationship between hours of sleep achieved and stress levels, they could make a judgement using descriptive statistics.
However, what if a researcher wanted to examine how changes in social support over time impact stress? In the latter case, more advanced statistical methods would be required. Further, multivariate analysis in psychology can look at the relationships between multiple variables at the same time. In other words, the research question and type of methods determine the type of analysis required.
Let’s take a look at some forms of advanced statistics in mental health research and when it’s appropriate to use them.
A regression analysis is suitable for occasions in which you want to determine which independent variables have the biggest impact on dependent or outcome variables. For example, if you wanted to figure out how much factors like screen time, sleep quality, and perceived social support affect depression levels. In this case, a regression analysis allows you to control for and determine how much each of these variables contributes to symptoms.
ANOVAs allow researchers to look at multiple variables at once and determine which combination creates the biggest impact on outcomes. It gives information on whether means from three or more groups have a significant effect. For example, if a study aimed to determine whether depression symptoms differed in groups of people with high, moderate, or low levels of social support, they would use an ANOVA to see which group’s means differ from the others. The technique is similar to regression analyses, except that it has discrete independent variables. Both are elements of General Linear Modeling.
This approach works to simplify complex correlated variables into a few latent, or unobserved, variables called ‘factors.’ For instance, if researchers wanted to determine which hidden factors contributed to outcomes on a mental health screener, they would use a factor analysis to measure related constructs.
Similar to factor analysis, cluster analysis is used to group items together. However, while factor analysis aims to group variables, cluster analysis involves observations of behaviors based on similar features. Further, with a cluster analysis, no pre-defined categories exist. Instead, groupings naturally arise from the data. For example, in segmentation studies, a cluster analysis can be used to see whether certain demographic or psychological groups arise.
A logistic regression is a form of regression analysis that is used when dependent variables take the form of one of two categorical variables (such as ‘yes’ and ‘no’) and the independent variables are categorical or continuous. For instance, if you wanted to determine whether students’ stress levels, sleep quality, and perceived social support are predictive of whether they graduate, a logistic regression would be used.
Advanced statistics such as these are commonly used statistical methods in psychology, but they’re often prone to issues like subjectivity, multicollinearity, and alpha inflation. For this reason, a certain level of expertise is often required when designing a study and interpreting the data. This is where professional support for mental health study design and statistics can help.
Professional data analysis for mental health research can ensure that the right statistical methods are used, results are interpreted correctly, the research question is answered, and the study is replicable.
The following are some of these benefits in more detail:
Your hypothesis is your testable prediction about outcomes of a study – and it dictates the type of psycholical tools and analysis used. In fact, a clear hypothesis is the foundation for all subsequent actions. Without a specific hypothesis, research can lack focus and lead to either inconclusive or illogical results. Professional guidance can ensure that your hypothesis leads you to causality rather than correlation, setting the stage for innovation and growth.
Data cleaning transforms raw data into usable, high-quality data for analysis, which is essential for accurate, valid, and replicable results.2 This might mean removing outliers, dealing with incorrect or missing data, or assessing for normality.2 Professional support can practically help or guide researchers and clinicians in rigorous data-cleaning procedures according to a regimented framework.
When the hypothesis is clear and the data is cleaned, the next step is choosing the right data analysis test. This will be based on your hypothesis and methods, and requires knowledge of software packages, like SPSS/R data analysis for mental health. Professional support can simplify complex statistical analysis, helping you understand outcomes and make an impact through your efforts.
Replicability is key to successful psychology research, as it builds confidence in the meaning of the findings. Therefore, if your study can be reproduced with similar results, it successfully builds on a knowledge base.3 Professional support can make sure that your methods are transparent, accurately analysed, and disseminated clearly.
Whether you’re looking for support with advanced statistics for mental health research or psychometrics and statistical testing, PsychWrite can help. Our team of mental health professionals is highly experienced and qualified in research methods and statistical analysis, meaning we can provide statistical consulting for clinicians, dissertations, and organisations.
If you need help with refining your hypothesis, selecting appropriate methods and tests, statistical interpretation for psychology research, or writing results, contact PsychWrite today. We can ensure that your endeavors align with the highest academic and ethical standards, and that your study is supported from start to finish.
