From 38ad37aed2c73c6e22742149e482e44d606d5cbb Mon Sep 17 00:00:00 2001
From: "Ranjit K. Singh" <101261422+r-k-singh@users.noreply.github.com>
Date: Mon, 22 Jan 2024 15:51:14 +0100
Subject: [PATCH] Update README.md

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 README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 995fae0..5a9ae1d 100644
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+++ b/README.md
@@ -18,11 +18,11 @@ pak::pak("https://github.com/MatRoth/questionlink")
 ``` 
 
 ## Getting started
-We would strongly recommend working through the [questionlink tutorial](https://matroth.github.io/questionlink/articles/questionlink_tutorial.html)before using the package. 
+We would strongly recommend working through the [questionlink tutorial](https://matroth.github.io/questionlink/articles/questionlink_tutorial.html) before using the package. 
 Applying the QuestionLink package is easy, but to ensure valid harmonization solutions you need to understand the methodological underpinnings and the necesary assumptions.
 
 However, before you invest the time to understand our package, note that there may be alternatives for your specific case:
 
-[ ] If you want to harmonize two psychometric multi-item scales, which have at least some identical items in common, consider NEAT Equating (Non-equivalent Groups with Anchor Tests Equating). 
-[ ] If you only want to harmonize two single-item instruments and you have a split-half experiment varying the two, simply apply the [Equate Package](https://github.com/talbano/equate) directly.
-[ ] If you have a dataset, where all respondents answered both instruments (ideally in random order), then consider applying a [calibrated multiple imputation approach](https://doi.org/10.1002/sim.6562) instead.
+- If you want to harmonize two psychometric multi-item scales, which have at least some identical items in common, consider NEAT Equating (Non-equivalent Groups with Anchor Tests Equating). 
+- If you only want to harmonize two single-item instruments and you have a split-half experiment varying the two, simply apply the [Equate Package](https://github.com/talbano/equate) directly.
+- If you have a dataset, where all respondents answered both instruments (ideally in random order), then consider applying a [calibrated multiple imputation approach](https://doi.org/10.1002/sim.6562) instead.