Why Choose Our Quantitative Data Analysis Service?
Certified statisticians, domain-expert analysts and PhD research consultants — delivering accurate, reproducible results with fully interpreted reports since 2004.
SPSS, R, Stata, AMOS & SmartPLS
All major statistical platforms covered — with labelled outputs, interpreted tables and academic narrative ready for submission.
SEM — CB-SEM & PLS-SEM
Full Structural Equation Modelling — measurement model, model fit indices (CFI, RMSEA, SRMR), path coefficients and bootstrapped mediation.
Hypothesis Testing
Normality, homogeneity, t-test, ANOVA, MANOVA, Chi-square, correlation, regression — with p-values and effect sizes properly reported.
Full Results Chapter
Complete Chapter 4 / Results & Discussion written in academic prose — tables, figures, APA / IEEE formatting and interpretation included.
Fast Turnaround
Basic analysis in 48–72 hours. Full SEM or panel data analysis in 5–7 working days. Rush delivery available on request.
100% Confidential
Your data and research are fully protected under strict non-disclosure. We do not share, publish or reuse any client data.
What is Quantitative Data Analysis?
Quantitative data analysis is the systematic process of examining, cleaning, and interpreting numerical data using statistical techniques and mathematical models. It transforms raw survey responses, experimental measurements, financial records, or secondary datasets into meaningful insights — through descriptive statistics (mean, median, standard deviation, frequency distribution), inferential statistics (t-tests, ANOVA, regression, chi-square), and advanced multivariate methods (SEM, factor analysis, cluster analysis, panel data modelling). In PhD and academic research, quantitative analysis underpins the validity of research hypotheses, substantiates theoretical frameworks, and produces publishable findings across Management, Social Sciences, Engineering, Medical Research and Economics.
Quantitative Data Analysis Services — What We Offer
End-to-end statistical analysis support — from data cleaning and coding to results writing and journal-ready outputs.
Descriptive & Inferential Statistics
Complete descriptive analysis and hypothesis-driven inferential testing — interpreted in academic language for dissertation Chapter 4.
- Frequency distribution, mean, median, SD, skewness, kurtosis
- Independent & paired sample t-tests
- One-way & two-way ANOVA / MANOVA / ANCOVA
- Chi-square test of independence and goodness-of-fit
- Pearson & Spearman correlation analysis
SEM — AMOS (CB-SEM) & SmartPLS (PLS-SEM)
Full structural equation modelling — from model specification and CFA to path analysis, fit indices and bootstrapped mediation/moderation.
- Measurement model — CFA, factor loadings, Cronbach α, CR, AVE
- Discriminant validity (HTMT, Fornell-Larcker criterion)
- Structural model — path coefficients, R², Q², f²
- Model fit — CFI, TLI, RMSEA, SRMR, GFI, AGFI
- Mediation & moderation (bootstrapping, PROCESS Macro)
Regression Analysis
Simple, multiple, hierarchical, logistic and panel regression — with full diagnostic testing (VIF, DW, heteroscedasticity) and interpretation.
- Simple & multiple linear regression (OLS)
- Hierarchical regression — moderated/mediated regression
- Binary & multinomial logistic regression
- Ordinal regression / Poisson regression
- VIF multicollinearity, Durbin-Watson, residual diagnostics
Factor Analysis — EFA & CFA
Exploratory and Confirmatory Factor Analysis using SPSS, R (lavaan/psych) or AMOS — with scree plots, factor loadings, KMO and Bartlett's tests.
- EFA — Principal Component Analysis / Principal Axis Factoring
- Varimax, Promax, Oblimin rotation
- KMO & Bartlett's test of sphericity
- CFA — model fit, modification indices, factor loadings
- Scale reliability and construct validity reporting
Panel Data & Time Series Analysis
Advanced econometric and longitudinal data modelling using Stata, R or EViews — suitable for Economics, Finance and Social Science PhD research.
- Fixed effects & random effects panel models (Hausman test)
- GMM (Arellano-Bond) for dynamic panel data
- Unit root tests — ADF, PP, KPSS
- Cointegration — Johansen, ARDL bounds test
- ARIMA, VAR, VECM, GARCH time series models
Data Cleaning, Coding & Report Writing
End-to-end data preparation and full academic report writing — from raw Likert survey data to a polished Results chapter ready for dissertation or journal submission.
- Data entry, coding, reverse scoring, missing value treatment
- Outlier detection (Mahalanobis distance, box plots)
- Normality testing — Kolmogorov-Smirnov, Shapiro-Wilk
- Full results chapter in APA / IEEE / Vancouver style
- Tables, charts and figures formatted for journal submission
Statistical Tools & Software We Use
Industry-standard quantitative analysis platforms — selected based on your research design, data type, university norms and journal target.
IBM SPSS Statistics
R & RStudio
Stata
AMOS (CB-SEM)
SmartPLS (PLS-SEM)
PROCESS Macro (Hayes)
Python (scipy/statsmodels)
EViews
JASP
LISREL
MS Excel
SAS
NVivo (Mixed Methods)
Our Quantitative Analysis Process — Step by Step
A structured, transparent workflow from data receipt to final submission-ready report.
01
Data Receipt & Requirement Discussion
Share your dataset (SPSS .sav, Excel, CSV, Stata .dta) and objectives. We review your questionnaire, research hypotheses, and university/journal requirements, then confirm the appropriate statistical tests and tools.
02
Data Cleaning, Coding & Pre-Analysis Tests
Missing value treatment, reverse coding, outlier detection (Mahalanobis distance), normality testing (KS / Shapiro-Wilk), and variance inflation factor (VIF) checks — ensuring data integrity before analysis begins.
03
Reliability & Validity Testing
Cronbach's alpha, composite reliability (CR), average variance extracted (AVE), convergent validity and discriminant validity (HTMT ratio, Fornell-Larcker criterion) — with SPSS / AMOS / SmartPLS outputs and narrated interpretation.
04
Core Statistical Analysis
Execution of all agreed analyses — descriptive statistics, regression, ANOVA, factor analysis, SEM, mediation/moderation, panel data models — with fully labelled outputs in SPSS, AMOS, SmartPLS, R, Stata or EViews.
05
Results Interpretation & Academic Report Writing
All outputs interpreted in plain academic language — each hypothesis decision stated, tables and figures formatted in APA / IEEE / Chicago style, and a complete Results & Discussion chapter written ready for your dissertation or journal manuscript.
06
Revision & Examiner / Reviewer Response Support
Free revisions for any analytical queries from examiners or journal reviewers — reanalysis, additional tests or rewriting of the results section as required for viva or peer review.
Quantitative Analysis Techniques by Domain & Tool
Common PhD and MBA research analysis types — with recommended tools and typical application domains.
| # | Analysis Technique | Recommended Tools | Output / Deliverable | Level |
|---|---|---|---|---|
| 01 | Management
PLS-SEM with Mediation & Moderation (SmartPLS)
TAM, TPB, TRI, Service Quality models — testing indirect effects with bootstrapping for HR, Marketing and Strategy dissertations.
PLS-SEMMediationModeration
|
SmartPLSSPSS | Path model, bootstrapped CIs, full Chapter 4 | PhD MBA |
| 02 | Management
CB-SEM using AMOS — Confirmatory Factor Analysis & Path Model
Full structural equation modelling for scale validation and theory testing — model fit indices CFI, TLI, RMSEA, SRMR.
CB-SEMCFAAMOS
|
AMOSSPSS | CFA output, SEM path diagram, fit indices table | PhD |
| 03 | Management / HR
Multiple Regression & Hierarchical Regression (SPSS)
Testing influence of independent variables on organisational outcomes — employee performance, job satisfaction, turnover intention models.
Multiple RegressionVIFBeta Coefficients
|
SPSSR | Regression table, R², F-test, residual plots | PhD / MBA |
| 04 |
Exploratory Factor Analysis (EFA) for Scale Development
Principal Component Analysis / Principal Axis Factoring with Varimax rotation for attitude, perception and behaviour questionnaire validation.
EFAPCAKMO / Bartlett
|
SPSSR (psych) | Scree plot, rotated component matrix, reliability table | PhD |
| 05 |
ANOVA, MANOVA & Post-Hoc Tests
Group comparison analysis for educational outcomes, demographic differences and intervention effectiveness — with Tukey, Bonferroni post-hoc tests.
ANOVAMANOVAPost-Hoc
|
SPSSStata | ANOVA table, Levene's test, effect size (η²) | PhD / MBA |
| 06 | Medical / Health Sciences
Binary & Multinomial Logistic Regression
Predicting clinical outcomes, disease risk factors, patient mortality and health behaviour — odds ratios, ROC curve, Hosmer-Lemeshow test.
Logistic RegressionOdds RatioROC Curve
|
SPSSRStata | Odds ratio table, ROC plot, classification report | PhD |
| 07 | Medical Research
Survival Analysis — Kaplan-Meier & Cox Proportional Hazards
Time-to-event analysis for clinical trials, cancer survival, readmission rates — log-rank test and hazard ratio estimation.
Survival AnalysisKaplan-MeierCox Regression
|
R (survival)StataSPSS | K-M curves, log-rank table, Cox HR output | PhD |
| 08 | Economics / Finance
Panel Data Analysis — Fixed Effects, Random Effects & GMM
Longitudinal econometric modelling for corporate finance, macroeconomic and banking research — Hausman test, Arellano-Bond GMM estimator.
Panel DataFixed EffectsGMM
|
StataR (plm) | Panel regression tables, Hausman test, GMM output | PhD |
| 09 | Economics / Finance
Time Series Analysis — ARIMA, VAR, VECM & GARCH
Forecasting and causality analysis for macroeconomic indicators, stock returns, exchange rates — ADF unit root, Johansen cointegration, Granger causality.
ARIMAGARCHVAR / VECM
|
EViewsR (forecast)Stata | ADF tables, impulse response, GARCH volatility plot | PhD |
| 10 | Engineering / CSE
Performance Evaluation — Statistical Hypothesis Testing & Comparative Analysis
t-tests, Wilcoxon signed-rank test, Friedman test and effect size (Cohen's d) for algorithm benchmarking and system performance comparison in CSE, ECE and Biomedical research.
t-testWilcoxonCohen's d
|
RPythonSPSS | Test statistics table, effect size, box plots | PhD |
| 11 | Management / Marketing
Mediation & Moderation — Hayes PROCESS Macro
Bootstrap-based conditional process analysis for indirect effects, moderated mediation and mediated moderation — PROCESS Models 4, 6, 7, 14.
PROCESS MacroBootstrappingIndirect Effect
|
SPSSR (mediation) | Bootstrap CIs, conditional effects table, Johnson-Neyman plot | PhD / MBA |
| 12 |
Chi-Square Test, Kruskal-Wallis & Mann-Whitney U
Non-parametric tests for categorical and ordinal data — association between demographic variables and research outcomes in education, sociology and public health research.
Chi-SquareNon-ParametricMann-Whitney
|
SPSSR | Contingency table, asymptotic significance, Cramér's V | PhD / MBA |
Frequently Asked Questions — Quantitative Data Analysis
Common queries from PhD scholars, MBA researchers and academic professionals about our statistical analysis services.
What quantitative data analysis tools do you use for PhD research?
We use IBM SPSS Statistics, R (with packages lavaan, psych, ggplot2, lme4), Stata, AMOS for covariance-based SEM (CB-SEM), SmartPLS for variance-based PLS-SEM, Hayes PROCESS Macro for mediation and moderation, Python (pandas, scipy, statsmodels), EViews for econometric analysis, JASP, LISREL, and SAS — selected based on your research design, data type and university preference. We provide outputs in the format required by your institution or target journal.
What is the difference between AMOS (CB-SEM) and SmartPLS (PLS-SEM), and which should I use?
AMOS uses covariance-based SEM (CB-SEM), which is appropriate when your goal is theory confirmation, your constructs are reflective, sample sizes are moderate to large (200+), and data is multivariate normal. SmartPLS uses variance-based PLS-SEM, which is better for exploratory research, small samples, formative constructs, and prediction-oriented models. Both are widely accepted for PhD dissertations and journal publications. We advise on the best choice based on your research objectives, model type and domain.
Do you provide complete SPSS output with interpretation for the dissertation?
Yes. We provide fully labelled SPSS outputs for every analysis — frequency tables, descriptive statistics, Cronbach's alpha, correlation matrix, regression coefficients, ANOVA tables, factor loadings and more — along with a complete written interpretation in academic prose formatted to APA or your university style, ready to paste into your dissertation Chapter 4.
Can you analyse my survey data for a Management or MBA dissertation?
Yes. We analyse Likert-scale survey data for MBA and PhD dissertations across all management disciplines — HR, Marketing, Finance, Operations, Strategy, Entrepreneurship and Healthcare Management. Services include reliability analysis, EFA, CFA, SEM (AMOS / SmartPLS), regression, mediation/moderation (PROCESS Macro), ANOVA, and a complete Results chapter. We support all Indian universities including IIM-affiliated, Symbiosis, XLRI, BITS, Amity, Manipal, and VTU.
How long does quantitative data analysis take?
Turnaround depends on analysis complexity. Basic descriptive and inferential statistics (SPSS) — 48 hours. Full reliability, validity, regression and ANOVA package — 3–4 working days. Complete SEM analysis (AMOS or SmartPLS) with mediation/moderation and written chapter — 5–7 working days. Panel data or time series econometric analysis (Stata / EViews) — 6–8 working days. Rush delivery is available. We confirm exact timelines after reviewing your dataset and requirements.
Do you support analysis for Medical and Health Sciences research?
Yes. We conduct quantitative analysis for medical, public health and clinical research including logistic regression for risk factors, survival analysis (Kaplan-Meier, Cox proportional hazards), diagnostic accuracy (ROC, sensitivity, specificity, AUC), ANOVA for clinical trial comparisons, and epidemiological statistics. We use SPSS, R, Stata and MATLAB and provide outputs suitable for journal submission to PubMed-indexed, Scopus and SCI journals.
Will you write the methodology and results chapters based on the analysis?
Yes. We write the complete Research Methodology chapter (Chapter 3) covering research philosophy, research design, sampling, data collection instrument, validity and reliability, and chosen statistical methods — as well as the Results and Discussion chapter (Chapter 4/5) with all tables, figures and academic interpretation. Both chapters are written to your university format (VTU, Anna University, JNTU, SRM, Manipal etc.) and are plagiarism-checked below 10% using Turnitin.
Statistical Analysis Lab — Bangalore
A glimpse inside our quantitative data analysis centre in Bangalore — SPSS workstations, R and Stata terminals, SEM analysis desks and dedicated PhD research consultation rooms.
SPSS Analysis Workstation
R / Stata Statistical Lab
AMOS / SmartPLS SEM Desk
PhD Data Analysis Consultation
Results Chapter Writing
Panel & Time Series Analysis