Descriptive statistics and cross-tabulation analysis plan
Clarifying what and whom to compare before tabulation helps select variables and methods. Check nominal, ordinal or continuous scales before choosing statistics.
Descriptive statistics and cross-tabulation analysis plan
Basic information
Record the analytical purpose and data overview.
Completed example for this section (fictional)
This fictional example is for explanation. Adapt it to your own program. Your entries will not change.
- Program or survey name
- Desk waiting times
- Observations: valid responses
- 5 cases (fictional)
- Evaluation question addressed
- How variable are waiting times?
- Analyst and date
- Staff member A / July 2026 (fictional)
Reason for this entry: Do not present these 5 cases as representative of the whole community.
Writing tips and explanation for this section
About this section
Record the analytical purpose and data overview.
Do not present these 5 cases as representative of the whole community.
View the method explanationWriting tips
Follow each field’s heading to record the program name, focus, period, and other details. Leave undecided matters clearly marked as unresolved.
Read the full fictional exampleVariables and measurement scales
List analyzed variables and check scales and statistics.
Completed example for this section (fictional)
This fictional example is for explanation. Adapt it to your own program. Your entries will not change.
Scroll horizontally to view the table
| Variable | Scale: nominal / ordinal / continuous | Missing count and treatment | Statistics: mean / median / frequency, etc. | Notes |
|---|---|---|---|---|
| Waiting time | Continuous quantity in minutes | 0 missing; record any missing values separately | Mean 20 minutes, median 10 minutes, range 5–70 minutes | Values: 5,5,10,10,70 (fictional) |
Reason for this entry: Check records rather than automatically deleting extreme values.
Scroll horizontally to view the table
| Variable | Scale: nominal / ordinal / continuous | Missing count and treatment | Statistics: mean / median / frequency, etc. | Notes |
|---|---|---|---|---|
Writing tips and explanation for this section
About this section
List analyzed variables and check scales and statistics.
Check records rather than automatically deleting extreme values.
View the method explanationWriting tips
Keep the information in each row aligned. Scroll wide tables horizontally to see every column.
Read the full fictional exampleCross-tabulation design
Plan combinations for cross-tabulations.
Completed example for this section (fictional)
This fictional example is for explanation. Adapt it to your own program. Your entries will not change.
Scroll horizontally to view the table
| Row variable | Column variable | Purpose: what to compare? | Cell display: count / row % / column % | Statistical test needed? |
|---|---|---|---|---|
| Reception method | Waiting time category | Do waits differ by booking status? | Counts and percentages with each row as denominator | No inference with these 5 cases |
Reason for this entry: Characteristic data are separately needed, so this is a plan.
Scroll horizontally to view the table
| Row variable | Column variable | Purpose: what to compare? | Cell display: count / row % / column % | Statistical test needed? |
|---|---|---|---|---|
Writing tips and explanation for this section
About this section
Plan combinations for cross-tabulations.
Characteristic data are separately needed, so this is a plan.
View the method explanationWriting tips
Keep the information in each row aligned. Scroll wide tables horizontally to see every column.
Read the full fictional exampleEffect-size plan for intervention evaluation
Plan effect sizes where before-and-after or between-group comparisons are included.
Completed example for this section (fictional)
This fictional example is for explanation. Adapt it to your own program. Your entries will not change.
Scroll horizontally to view the table
| Variables compared | Groups: before/after, intervention/control, etc. | Effect-size measure: Cohen's d, odds ratio, etc. | Calculation tool |
|---|---|---|---|
| No intervention comparison here | Not applicable | Not applicable: descriptive tabulation | Check formulas in a spreadsheet. |
Reason for this entry: Effect sizes and p-values are not required for every descriptive summary.
Scroll horizontally to view the table
| Variables compared | Groups: before/after, intervention/control, etc. | Effect-size measure: Cohen's d, odds ratio, etc. | Calculation tool |
|---|---|---|---|
Writing tips and explanation for this section
About this section
Plan effect sizes where before-and-after or between-group comparisons are included.
Effect sizes and p-values are not required for every descriptive summary.
View the method explanationWriting tips
Keep the information in each row aligned. Scroll wide tables horizontally to see every column.
Read the full fictional exampleCompletion check
Completed example for this section (fictional)
This fictional example is for explanation. Adapt it to your own program. Your entries will not change.
- ☑ Checked: Checked scales and used appropriate statistics
- ☑ Checked: Decided and recorded missing-data handling: exclusion or imputation
- ☑ Checked: Used consistent row or column percentage directions
- ☑ Checked: Checked distributions of variables summarized by means using appropriate plots or value inspection
- ☑ Checked: Specified scale and population when summarizing differences, and reported uncertainty appropriate to design and assumptions for inference; recorded reasons where not applicable
- ☑ Checked: Prepared chart titles, axis labels and notes
Reason for this entry: Assume the distribution was checked by listing 5 values. Item 5 is checked because reasons for excluding intervention comparisons and inference are recorded above, not because a p-value was calculated.
Writing tips and explanation for this section
About this section
Assume the distribution was checked by listing 5 values. Item 5 is checked because reasons for excluding intervention comparisons and inference are recorded above, not because a p-value was calculated.
View the method explanationWriting tips
Read each item and check those you have confirmed. Do not use the fictional example’s checkbox states as findings about your own program.
Read the full fictional exampleBefore writing, check when to use the worksheet and what completion involves
Descriptive statistics and cross-tabulation analysis plan is a worksheet for applying Descriptive Statistics and Cross-tabulation in meetings or individual work. For the overall duration of the method, use “Planning example, not a universal standard duration: several hours–2 days, including data cleaning, tabulation and charts” as a reference, adjusting for scale and resources. Fill in what you know provisionally and leave unresolved matters as questions to check next.
When this worksheet is useful
- When obtaining and reporting an initial overview after collecting survey or monitoring data
- When comparing service reach or satisfaction by beneficiary characteristics, such as gender, age or area
- When preparing numerical summaries and charts as a basis for an evaluation report
Participants and preparation
1–2 evaluators working with data collectors and, where separate, statistical staff
Bring the materials used for decisions, existing data, and differing stakeholder views to help complete the worksheet.
Suggested order
- 1. Basic informationRecord the analytical purpose and data overview.
- 2. Variables and measurement scalesList analyzed variables and check scales and statistics.
- 3. Cross-tabulation designPlan combinations for cross-tabulations.
- 4. Effect-size plan for intervention evaluationPlan effect sizes where before-and-after or between-group comparisons are included.
- 5. Completion check
Signs the worksheet is ready for review
- Frequency distributions and descriptive statistics
- Cross-tabulations by characteristics or groups
- Effect-size worksheet for before-and-after comparisons
Cautions for review
- Reporting means without checking distributional skewness or outliers. Always examine histograms or box plots and add medians where necessary.
- Confusing small p-values with important findings. Even tiny differences may be significant in large datasets. Always report effect sizes and confidence intervals together.
- Do not compare differently sized groups solely by counts. Show counts, denominators and percentages, including instability of percentages in small groups.
Read a completed fictional example (your entries will not change)
A fictional illustrative example with 5 waiting times of 5, 5, 10, 10 and 70 minutes.
Basic information
- Program or survey name
- Desk waiting times
- Observations: valid responses
- 5 cases (fictional)
- Evaluation question addressed
- How variable are waiting times?
- Analyst and date
- Staff member A / July 2026 (fictional)
Reason for this entry: Do not present these 5 cases as representative of the whole community.
Variables and measurement scales
Scroll horizontally to view the table
| Variable | Scale: nominal / ordinal / continuous | Missing count and treatment | Statistics: mean / median / frequency, etc. | Notes |
|---|---|---|---|---|
| Waiting time | Continuous quantity in minutes | 0 missing; record any missing values separately | Mean 20 minutes, median 10 minutes, range 5–70 minutes | Values: 5,5,10,10,70 (fictional) |
Reason for this entry: Check records rather than automatically deleting extreme values.
Cross-tabulation design
Scroll horizontally to view the table
| Row variable | Column variable | Purpose: what to compare? | Cell display: count / row % / column % | Statistical test needed? |
|---|---|---|---|---|
| Reception method | Waiting time category | Do waits differ by booking status? | Counts and percentages with each row as denominator | No inference with these 5 cases |
Reason for this entry: Characteristic data are separately needed, so this is a plan.
Effect-size plan for intervention evaluation
Scroll horizontally to view the table
| Variables compared | Groups: before/after, intervention/control, etc. | Effect-size measure: Cohen's d, odds ratio, etc. | Calculation tool |
|---|---|---|---|
| No intervention comparison here | Not applicable | Not applicable: descriptive tabulation | Check formulas in a spreadsheet. |
Reason for this entry: Effect sizes and p-values are not required for every descriptive summary.
Completion check
- ☑ Checked: Checked scales and used appropriate statistics
- ☑ Checked: Decided and recorded missing-data handling: exclusion or imputation
- ☑ Checked: Used consistent row or column percentage directions
- ☑ Checked: Checked distributions of variables summarized by means using appropriate plots or value inspection
- ☑ Checked: Specified scale and population when summarizing differences, and reported uncertainty appropriate to design and assumptions for inference; recorded reasons where not applicable
- ☑ Checked: Prepared chart titles, axis labels and notes
Reason for this entry: Assume the distribution was checked by listing 5 values. Item 5 is checked because reasons for excluding intervention comparisons and inference are recorded above, not because a p-value was calculated.
Common errors in judgment
Replacing non-response with 0 before averaging
How to revise: State missing counts and treatment and use valid responses as the denominator.
Reason: An observed zero differs from missing data.
Mixing denominators in characteristic-specific percentages
How to revise: State row or column percentages in the table.
Reason: Identical counts answer different questions depending on denominators.
References and checked sections
- NIST/SEMATECH e-Handbook: Measures of LocationNIST
- Section consulted
- Definition of Location / Why Different Measures
- How it relates to the text
- Interpret summary statistics by comparing means and medians when distributions are skewed or contain extreme values.
- NIST/SEMATECH e-Handbook 7.4.5: How can we compare the results of classifying according to several categories?NIST
- Section consulted
- Contingency Table approach / Contingency table classifying defects / Column and Row probabilities
- How it relates to the text
- Basic contingency table structure: combine classifications in rows and columns and record counts in cells.
How to cite this page
Change history
- — Checked sources for definitions and applicability conditions and added fictional examples, judgment rationales and all-field examples. Revised unverified historical or adoption claims, and adjusted fixed counts, thresholds and institutional generalizations in procedures and cautions. Aligned distribution checks with listing small sets of values. Rechecked example and completion-check correspondence and conditional procedures. Matched cross-tabulation definitions to the relevant NIST contingency-table section, separately from mean and median sources.