Data Analysis - Question and answers

For my university thesis on customer satisfaction in e-commerce, I conducted an online survey with 300 respondents and in-depth interviews with 15 customers. The survey suggests high satisfaction scores, but interviews reveal underlying frustrations with shipping times. I've tried basic cross-tabulation in SPSS, but the discrepancies persist. I'm looking for practical methods to reconcile this data without oversimplifying.

I work as an HR analyst for a company with over 1,000 employees. We use an HRM system that collects engagement survey data quarterly, but the responses have a lot of missing entries and varying scales (e.g., some use 1-5, others use 1-10). I've tried manual cleaning in Excel, but it's error-prone and time-consuming. I need a systematic approach to handle this big collection of data for accurate insights.

I'm a sociology researcher focusing on youth culture and digital interactions. I plan to investigate how social media affects teenagers' social behaviors, but I'm unsure whether to use structured surveys (quantitative) or in-depth interviews (qualitative). I've read about both methods, but I need practical advice on which yields better insights for proposing community interventions. My constraints include a limited budget and a sample size of about 200 participants from urban schools.

I've collected data from government reports, academic papers, and NGO surveys on movements like the literacy campaign and women's empowerment initiatives in Kerala. However, when I try to correlate this with recent election results and policy changes, the patterns seem inconsistent. I've attempted using basic statistical tools, but I'm unsure if my methodology is robust enough for a comprehensive analysis. Any advice on practical approaches or tools would be greatly appreciated.

I'm analyzing data from a customer feedback survey for a retail business. The survey has Likert-scale questions (quantitative) and open-ended comments (qualitative). I've coded the qualitative data into themes, but I'm unsure how to combine them with the quantitative results to draw comprehensive conclusions. I'm using SPSS for quantitative analysis and manual coding for qualitative, but I feel like I'm missing connections.

I'm a graduate student focusing on comparative politics, and I've gathered census data and election results, but I'm struggling with integrating them to show causal relationships. I've tried using simple correlation analysis in Excel, but it feels too simplistic. I also want to account for factors like education levels and media exposure. Any suggestions on specific statistical methods or software would be greatly appreciated.

I'm a PhD student focusing on personality development over time. My study involves tracking 200 participants over five years using the Big Five inventory and well-being surveys. About 15% have dropped out, and I'm concerned that listwise deletion might bias my results. I've tried using multiple imputation in SPSS, but I'm not sure if it's the best approach or if there are specific considerations for personality data.

I've collected survey data from 500 respondents across Mumbai, Delhi, and Bangalore, focusing on their caste background, education levels, income, and perceived social mobility. I'm using regression analysis, but the results show high multicollinearity between education and income, making it hard to isolate effects. I've tried controlling for age and gender, but still struggle with interpretation. My advisor suggested looking at longitudinal data, but I'm limited to cross-sectional data due to time constraints. Any tips on methodological improvements or alternative statistical techniques?