Qualitative Research in SPSS: Full Bachelor's Thesis

Identify qualitative variables such as nominal and ordinal, and quantitative variables such as discrete and continuous, and distinguish dependent from independent variables to frame hypotheses about perceived health.

Explore the Romanian subset of the 2014 European Health Survey, a 147-variable, 16,606-row database. Identify fv1 for fruit consumption and xz1 for perceived health, then outline data cleaning before analysis.

Plan and conduct a study by defining objectives, questions, relationships between dependent and independent variables. Select observational, experimental, longitudinal, cross-sectional approaches, collect data, analyze with statistical methods, and report implications.

Explain how to research a phenomenon by consulting previous studies and documentation, recode variables like HS1 for analysis, and apply correspondence analysis and multiple correspondence analysis in qualitative SPSS research.

Trim a 147-variable dataset to 11 variables plus bm1 and bm2 for weight and height. Remove minus one, minus two, and zero codes, then verify with filters and search.

Create a new variable by combining two variables into BMI in Excel, converting height from centimeters to meters, squaring height, and applying the BMI formula to analyze health status.

Import your Excel data into IBM SPSS. Inspect data and variable views to set types, label values, and designate measures (nominal, ordinal, or scale) for variables like sex and BMI.

Transform numerical BMI into an ordinal category in SPSS using recode into different variables, assign WHO-based categories—malnutrition, normal weight, overweight, obesity—and set the measure to ordinal.

Run chi square tests in SPSS to assess correlations between perceived health and each independent variable using Crosstabs, then compile a single table of results for your study.

Explore descriptive statistics to summarize and interpret data using graphical methods (pie charts, bar charts, histograms) and numerical measures (mean, median, mode, IQR, absolute and relative frequency) in SPSS.

Learn to create pie charts, bar charts, and histograms in SPSS for qualitative research, including selecting variables, adding data labels, titles, and colors.

Open the dataset in SPSS and perform descriptive analysis by generating numerical descriptives and frequency tables, then create pie charts to interpret variables like perceived health and alcohol consumption.

Build an interactive Excel dashboard by linking charts with a slicer to update visuals. Create pivot tables and select chart types, then color and add percentages for clear manager insights.

Create a professional dashboard in Power BI by importing an Excel dataset, cleaning headers, and building interactive charts—pie, donut, and treemap—with synchronized updates and polished visuals.

Explore factorial correspondence analysis in SPSS to reveal how perceived health relates to fruit consumption frequency, with interpretable tables, profiles, and chi-square insights.

Explore the theory of multiple factor correspondence analysis (MCA) as a dimensionality reduction method for many categorical variables. Use the Bert table and SVD to visualize relationships via scatter plots.

Fix a common SPSS MCA error by recoding zero-based variables to start at one. Transform, recode into different variables, adjust values, and verify in data view before analysis.

Learn how to recode variables in SPSS using theory and correspondence analysis, including creating clusters, labeling categories, and preparing data for final analysis.

Explore the practical use of multiple correspondence analysis in SPSS, interpret dimension reduction results, assess discrimination and multicollinearity, and visualize how dietary and socioeconomic variables relate to perceived health.

Merge two similar variables in spps? in SPSS by computing an arithmetic mean and rounding up to form a single variable, supporting dimension reduction and optimal scaling in analysis.

Learn practical binary logistic regression in SPSS, including coding the dependent variable as 0/1, setting reference categories, and interpreting model output.

Learn how to check for multicollinearity in SPSS using collinearity diagnostics and VIF, identifying when to remove problematic variables to stabilize regression estimates.

Evaluate a binary logistic model in SPSS by using the Hosmer-Lemeshow test to assess fit and the ROC curve with AUC to measure discrimination between healthy and unhealthy states.

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