Consultation Report — Research Service Example
Summary of Your Consultation
This report explains, in plain words, how we understood your study and how we plan to analyse it. It is based only on the data and the materials you sent us. For each hypothesis (a statement about your study that the analysis checks) in your study we plan a statistical test (a standard calculation that checks whether a pattern in your data is larger than chance alone would produce). Each section below explains one part of the plan, and the section “Decisions we made for you” lists every choice we made on your behalf, so you can check it before you approve.
| Item | Detail |
|---|---|
| Things your study measures | 3 |
| Hypotheses we will test | 1 |
| Statistical tests planned | 1 |
| How confident we are in this plan | High: we understood your study well |
What your study measures: study_year, academic_self_efficacy, learning_persistence
Decisions We Made for You
A statistical consultant decides whatever your materials and data already make clear, and explains each decision. These are the decisions we made on our own, and what each one is based on. If anything here does not match what you know about your study, tell us before you approve the quote and we will change it.
How we understood your study
For each thing your study measures: the part it plays in your study and what kind of answers it holds. Together these decide which test fits each hypothesis.
- Your study design — a single group of respondents, measured once.
- d1 — a factor that may explain or predict the outcome; its answers are ordered categories (for example, low / medium / high).
- q1_1 — one question among a group of questions analysed together; its answers are ordered categories (for example, low / medium / high).
- q1_2 — one question among a group of questions analysed together; its answers are ordered categories (for example, low / medium / high).
- q1_3 — one question among a group of questions analysed together; its answers are ordered categories (for example, low / medium / high).
- q1_4 — one question among a group of questions analysed together; its answers are ordered categories (for example, low / medium / high).
- q1_5 — one question among a group of questions analysed together; its answers are ordered categories (for example, low / medium / high).
- q2_1 — one question among a group of questions analysed together; its answers are ordered categories (for example, low / medium / high).
- q2_2 — one question among a group of questions analysed together; its answers are ordered categories (for example, low / medium / high).
- q2_3 — one question among a group of questions analysed together; its answers are ordered categories (for example, low / medium / high).
- q2_4 — one question among a group of questions analysed together; its answers are ordered categories (for example, low / medium / high).
- q2_5 — one question among a group of questions analysed together; its answers are ordered categories (for example, low / medium / high).
- academic_self_efficacy — a factor that may explain or predict the outcome; its answers are numbers on a scale (for example, the average of 1-to-5 answers).
- learning_persistence — the outcome: what your study tries to explain; its answers are numbers on a scale (for example, the average of 1-to-5 answers).
Checking Your Data File
We opened your data file and checked that it can be read in full.
| Item | Detail |
|---|---|
| The file could be read | Yes |
| Rows (usually one per respondent) | 180 |
| Columns (usually one per question or detail) | 11 |
The Files You Sent
Each file you uploaded, and what we understood it to be.
| File | What it is | Version notes | How sure we are |
|---|---|---|---|
| data.csv | Data | — | Certain |
| study-brief.md | Instructions | — | Likely |
| questionnaire.docx | Questionnaire | — | Certain |
| codebook.md | Instructions | — | Certain |
Your Questions and How We Score Them
Every column of your data that is part of your study: the question it holds, the measure it belongs to, and whether its scoring is reversed (see “Decisions we made for you”).
| Question | Column in your file | Group of questions | Scoring reversed |
|---|---|---|---|
| Year of undergraduate study | d1 | — | No |
| I can understand new course material even when it is complex. | q1_1 | academic_self_efficacy | No |
| I know how to approach an unfamiliar academic assignment. | q1_2 | academic_self_efficacy | No |
| I can find a way to resolve a difficulty in my studies. | q1_3 | academic_self_efficacy | No |
| I believe I can meet the requirements of my courses. | q1_4 | academic_self_efficacy | No |
| I can use feedback to improve my academic work. | q1_5 | academic_self_efficacy | No |
| I continue working on an assignment when it takes longer than I expected. | q2_1 | learning_persistence | No |
| After an unsuccessful attempt, I return to try again. | q2_2 | learning_persistence | No |
| I set aside regular time to make progress on my coursework. | q2_3 | learning_persistence | No |
| I complete study tasks even when my motivation is low. | q2_4 | learning_persistence | No |
| When course material is unclear, I keep looking for an explanation. | q2_5 | learning_persistence | No |
How We Name Your Columns
How each column of your data file is called in this report, what it belongs to, and the part it plays in your study.
| Column in your file | Name in this report | What it belongs to | Its part in the study |
|---|---|---|---|
| d1 | Year of undergraduate study | study_year | an explaining factor |
| q1_1 | I can understand new course material even when it is complex. | academic_self_efficacy | a question in a group |
| q1_2 | I know how to approach an unfamiliar academic assignment. | academic_self_efficacy | a question in a group |
| q1_3 | I can find a way to resolve a difficulty in my studies. | academic_self_efficacy | a question in a group |
| q1_4 | I believe I can meet the requirements of my courses. | academic_self_efficacy | a question in a group |
| q1_5 | I can use feedback to improve my academic work. | academic_self_efficacy | a question in a group |
| q2_1 | I continue working on an assignment when it takes longer than I expected. | learning_persistence | a question in a group |
| q2_2 | After an unsuccessful attempt, I return to try again. | learning_persistence | a question in a group |
| q2_3 | I set aside regular time to make progress on my coursework. | learning_persistence | a question in a group |
| q2_4 | I complete study tasks even when my motivation is low. | learning_persistence | a question in a group |
| q2_5 | When course material is unclear, I keep looking for an explanation. | learning_persistence | a question in a group |
| academic_self_efficacy | Academic self efficacy | academic_self_efficacy | a combined score |
| learning_persistence | Learning persistence | learning_persistence | a combined score |
Groups of Questions and Their Consistency
Your questionnaire has groups of questions that together measure one thing (each such group is called a scale). For each group: how many questions it has, how many of them are scored in reverse, and the range of answers.
| Group of questions | Questions | Scored in reverse | Range of answers |
|---|---|---|---|
| academic_self_efficacy | 5 | 0 | 1–5 |
| learning_persistence | 5 | 0 | 1–5 |
For every group whose range of answers is known, the analysis calculates its reliability (a number between 0 and 1, called Cronbach's alpha, that shows how consistently a group of questions measures one thing; the higher, the better).
Your Hypotheses and the Tests We Plan
The table below shows, for each hypothesis, which test we plan, what the test checks, and how strictly its result will be judged.
| Hypothesis | What it is about | Planned test | What the test checks | How strictly it is judged |
|---|---|---|---|---|
| H1 H1: Academic self efficacy is positively associated with learning persistence. | a link between two measures | Pearson correlation | Measures how strongly two numeric measures move together, and in which direction. | Main hypothesis: stricter check |
When several hypotheses are tested, we apply a correction for testing several hypotheses (when several hypotheses are tested, one of them can look convincing by chance alone; the correction makes each test slightly stricter to prevent that). Your main hypotheses get the stricter version (the Holm method); exploratory ones a lighter one (the Benjamini-Hochberg method). Which group a hypothesis belongs to depends only on where it came from, never on its result, and it is fixed in the analysis plan before any test runs. If a hypothesis is in the wrong group, now is the time to tell us.
Your Options, by What You Need
These options are arranged by the purpose you told us about (academic work): what is enough for a basic write-up, what we recommend for a thorough piece of work, and what you can add as an extension.
You can remove any selected test you do not want; its price is taken off and the quote is recalculated. A test marked “used only if the conditions are not met” is part of the test it replaces and is removed together with it, not on its own.
Enough for a basic write-up
| Test | Hypothesis | Kind | What it does | Your decision |
|---|---|---|---|---|
| Pearson correlation | H1 | The test we selected | Measures how strongly two numeric measures move together, and in which direction. | Not decided yet |
Recommended for a thorough piece of work
| Test | Hypothesis | Kind | What it does | Your decision |
|---|---|---|---|---|
| Spearman correlation | H1 | Used only if the conditions are not met | Runs instead of the Pearson correlation only if your data do not meet that test's conditions. Measures how strongly two measures move together when the answers are rankings or are unevenly spread. | Not decided yet |
Checking the Conditions of Each Test
Every test relies on a few assumptions (conditions a test needs from the data, for example that the scores are spread in a roughly bell-shaped way) about the data. We check them before any result is reported. If a condition is not met, a suitable alternative test that was chosen in advance runs instead, and we tell you.
Missing Answers and Number of Respondents
During the analysis we confirm, for each hypothesis, how many respondents can be included — the sample size (the number of respondents whose answers can be analysed). Missing answers (questions a respondent left unanswered) are handled by a rule fixed in advance; no respondent is removed without a recorded reason.
Respondents in your data file: 180
Unusual Answers
During the analysis we look for outliers (answers that lie far from all the others, such as a typing mistake) with a method suited to each test. Whatever we find is reported to you, and no answer is removed without a recorded reason.
Points to Confirm About Your Files and Names
There is nothing to confirm about your files or column names.