Research and Data Analysis Consulting

Ensuring your research and data is the best it can be.

Do you need help with:

1. Designing a study?

2. Literature review?

3. Forming hypotheses?

4. Selecting valid and reliable measures?

5. Sample size estimation?

6. Methods of data collection?

7. Data cleaning and quality checking?

8. Analysis and hypothesis testing?

9. Academic writing?

10. Data visualization?

Then I am here to help!

Hi, I’m Christopher Guadalupe, a graduate student in psychology with dual master’s degrees (M.S. in Quantitative Psychology and M.A. in Clinical Psychology) on the horizon (early 2025). With a B.S. in Psychology and extensive experience in research, including working in labs, conducting studies, and presenting at conferences, I am deeply committed to advancing the field through robust research practices.

I offer specialized consulting services to ensure the integrity and quality of your research. My approach focuses on preventing common pitfalls through meticulous planning and execution.

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90% of problems with published research are things that could be easily avoided in the planning and preparation phases as well as prior to running an analysis. My goal is to help you prevent these problems so your research can be as high quality as it can be.

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No matter what stage of a study you are in, if you would like assistance, then my goal is to help you in a way that your results can be trusted.​​

Below are my 5 primary skillsets regarding research design and data analysis.

1. Research Design and Methodology
Designing robust studies, including experimental and quasi-experimental designs.

Effective research design is essential for ensuring that study findings are valid, reliable, and actionable. My skill set in this area includes:
• Theoretical Foundation: Providing solid theoretical support to justify the study and ensure alignment with research objectives.
• Methodological Alignment: Ensuring that research questions are effectively addressed by the chosen methods, and that the study design aligns with the intended outcomes.
• Pre-Study Analysis Planning: Determining the appropriate analysis techniques and statistical methods before the study begins to avoid potential pitfalls and ensure comprehensive analysis.
• Hypothesis Specification & Integrity: Clearly formulating hypotheses and implementing strategies to prevent issues like p-hacking, thereby maintaining the integrity of the research findings.

2. Survey Design and Quality Assessment
Designing effective surveys and evaluating the quality of collected data.

Effective survey design and quality assessment ensure that data collection instruments are valid and reliable, leading to accurate and meaningful insights. It helps in obtaining high-quality data and ensures that research conclusions are based on sound measurement practices.
My skill set in this area focuses on:
• Survey Design: Assisting in crafting well-structured surveys that align with research goals, including selecting appropriate question formats and ensuring clarity and relevance of items.
• Measure Selection: Helping clients choose established measures and instruments that are suited to their specific research questions and objectives.
• Quality Assessment: Evaluating the quality of collected data by applying statistical measures such as Cronbach's alpha, skew, kurtosis, and normality to assess internal consistency and reliability of survey scales.

3. Data Cleaning and Preparation
Preparing and organizing data for accurate and meaningful analysis.

Proper data cleaning and preparation are crucial for ensuring the accuracy and integrity of the dataset. It eliminates errors and inconsistencies, making the data reliable and ready for meaningful analysis, thus improving the overall quality of research outcomes
My skill set in this area includes:
• Data Cleaning: Identifying and correcting errors or inconsistencies in the data, such as missing values, outliers, or duplicate entries, to ensure the dataset is accurate and reliable.
• Data Transformation: Converting raw data into a suitable format for analysis, which includes normalizing data, encoding categorical variables, and aggregating data as needed.
• Data Validation: Implementing checks and procedures to verify the accuracy and completeness of the data, ensuring that it meets the quality standards required for robust analysis.

4. Descriptive and Inferential Statistics
Analyzing data using descriptive, inferential, and advanced statistical methods.

My skill set in this area includes:
• Descriptive Statistics: Calculating and interpreting basic statistical measures such as means, medians, standard deviations, and frequencies to summarize and describe the main features of a dataset.
• Inferential Statistics: Applying statistical techniques to make inferences or predictions about a population based on sample data. This includes performing tests such as t-tests, ANOVA, chi-square tests, and confidence intervals to draw meaningful conclusions from data.
• Moderation Analysis: Assessing how the relationship between two variables is influenced by a third variable, thereby identifying interaction effects and understanding conditional relationships.
• Mediation Analysis: Exploring and explaining the process or mechanism underlying observed relationships between variables by examining how an intermediary variable (mediator) affects the relationship between an independent and a dependent variable.
• Data Interpretation: Translating statistical results into clear, actionable insights and helping clients understand the implications of their findings in the context of their research questions.

5. Data Visualization and Reporting
Presenting data effectively through visualization and comprehensive reporting.

Effective data visualization and reporting translate complex data into clear, understandable formats that facilitate communication of key findings. This enhances the ability of readers to interpret results, make informed decisions, and act on data-driven insights.

My skill set in this area includes:
• Data Visualization: Creating clear and insightful visual representations of data using tools like R, SPSS, and the Adobe Suite. This includes designing charts, graphs, and posters that highlight key findings and trends.
• Custom Visualizations: Developing tailored visualizations to address specific research questions or present complex data in an accessible format, helping to uncover patterns and insights.
• Reporting: Writing detailed reports that interpret data results, explain statistical findings, and discuss theoretical implications. This includes structuring reports to clearly communicate the implications of the data to various audiences.