القائمة
Guide for quantitative, qualitative, and mixed methods data analysis. Statistical test selection, interpretation, and tool recommendations (SPSS, R, Python).
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| name | data-analysis |
| version | 1.0.0 |
| description | Guide for quantitative, qualitative, and mixed methods data analysis. Statistical test selection, interpretation, and tool recommendations (SPSS, R, Python). |
| allowed-tools | ["Read","Write","Edit"] |
Guide untuk data analysis — quantitative, qualitative, dan mixed methods.
Checklist:
| Statistic | When to Use | Interpretation |
|---|---|---|
| Mean | Normally distributed continuous data | Average value |
| Median | Skewed data, outliers present | Middle value |
| Mode | Categorical data | Most frequent |
| SD | Variability of continuous data | Spread around mean |
| Range | Quick variability check | Min to max |
| Percentage | Proportions | Part of whole |
Reporting Format:
M = 25.4, SD = 3.2 (for normally distributed)
Mdn = 24, IQR = 18-28 (for skewed data)
Flowchart:
1. How many groups?
├── One group → One-sample t-test / Chi-square
├── Two groups →
│ ├── Independent → Independent t-test / Mann-Whitney U
│ └── Related/Paired → Paired t-test / Wilcoxon
└── Three+ groups →
├── Independent → ANOVA / Kruskal-Wallis
└── Related → Repeated measures ANOVA / Friedman
2. Variable relationships?
├── Two continuous → Pearson/Spearman correlation
├── Predict outcome → Regression (Linear/Logistic)
└── Multiple predictors → Multiple regression
Test Selection Table:
| Research Question | Variable Types | Test |
|---|---|---|
| Compare group means | 1 categorical (2 groups), 1 continuous | Independent t-test |
| Compare before-after | 2 related continuous | Paired t-test |
| Compare 3+ groups | 1 categorical (3+ groups), 1 continuous | One-way ANOVA |
| Association between variables | 2 categorical | Chi-square |
| Relationship strength | 2 continuous | Pearson/Spearman |
| Predict outcome | 1+ predictors, 1 outcome | Regression |
| Test | Assumptions | If Violated |
|---|---|---|
| t-test | Normality, homogeneity of variance | Use Mann-Whitney U |
| ANOVA | Normality, homogeneity, independence | Use Kruskal-Wallis |
| Pearson | Linearity, normality, homoscedasticity | Use Spearman |
| Regression | Normality, linearity, no multicollinearity | Transform data |
| Test | Effect Size | Small | Medium | Large |
|---|---|---|---|---|
| t-test | Cohen's d | 0.2 | 0.5 | 0.8 |
| ANOVA | η² (eta squared) | 0.01 | 0.06 | 0.14 |
| Correlation | r | 0.1 | 0.3 | 0.5 |
| Chi-square | Cramér's V | 0.1 | 0.3 | 0.5 |
Always report effect size! Not just p-value.
Template:
A [test] revealed [significant/non-significant] differences/effects/relationships
[description of finding], [df] = [value], p = [value], [effect size] = [value].
Example:
An independent samples t-test revealed a significant difference in test scores
between Group A (M = 78.5, SD = 5.2) and Group B (M = 72.1, SD = 6.8),
t(48) = 3.45, p = .001, d = 1.05, 95% CI [0.42, 1.68].
Types of Coding:
Software:
Braun & Clarke (2006) Six Steps:
| Criterion | Description |
|---|---|
| Credibility | Believable results; member checking |
| Transferability | Context-rich description |
| Dependability | Audit trail, consistent process |
| Confirmability | Researcher reflexivity |
Template:
Data were analyzed using thematic analysis following Braun and Clarke's
(2006) six-step approach. Three main themes emerged: [theme 1], [theme 2],
and [theme 3]. These themes were developed through iterative coding of [N]
[interviews/focus groups/documents].
| Strategy | When to Use |
|---|---|
| Merging | Compare quantitative + qualitative results side-by-side |
| Explanation | Qual explains quant findings |
| Exploration | Qual explores first, quant follows up |
| Embedding | One method supports the other |
| Tool | Best For | Pros | Cons |
|---|---|---|---|
| SPSS | Social sciences, beginners | GUI, widely accepted | Expensive, limited customization |
| R | Advanced stats, reproducibility | Free, powerful, flexible | Steep learning curve |
| Python | Large datasets, ML integration | Flexible, industry standard | Requires coding |
| JASP | Open-source alternative to SPSS | Free, user-friendly | Less features than SPSS |
| NVivo | Qualitative analysis | Comprehensive | Expensive |
| ATLAS.ti | Text/multimedia analysis | Flexible | Expensive |
DESCRIPTIVES VARIABLES=score /STATISTICS=MEAN STDDEV.
T-TEST GROUPS=group(1 2) /VARIABLES=score.
ONEWAY score BY group /STATISTICS DESCRIPTIVES.
REGRESSION /DEPENDENT outcome /METHOD=ENTER predictor1 predictor2.
# Descriptive
describe(data)
summary(data)
# t-test
t.test(group1, group2)
# ANOVA
aov(outcome ~ group, data)
# Correlation
cor.test(x, y)
# Regression
lm(outcome ~ predictor, data)
import pandas as pd
from scipy import stats
# t-test
stats.ttest_ind(group1, group2)
# ANOVA
stats.f_oneway(group1, group2, group3)
# Correlation
stats.pearsonr(x, y)
# Regression
from sklearn.linear_model import LinearRegression
Write academic abstracts for thesis, journal articles, and conference papers. Follow IMRAD or structured formats with appropriate length.
Check research ethics compliance for human subjects research. Covers consent, confidentiality, risk assessment, and approval requirements.
Academic proofreading for grammar, style, and consistency. Fix common errors while maintaining formal academic tone.
Select and justify research methodology. Compare approaches, choose designs, and write methodology chapters for thesis and research proposals.
Interpret quantitative and qualitative findings for Discussion sections. Explain results, relate to literature, and discuss implications.
Pre-submission checklist for thesis, journal articles, and conference papers. Format verification, requirement compliance, and final polishing.