Remove bold/italics

12-successful-survey-data-analysis.Rmd

@@ -61,7 +61,7 @@ example_des <-
 
 ## Introduction
 
-The previous chapters in this book aimed to provide the technical skills and knowledge required for running survey analyses. This chapter builds upon the previously mentioned best practices to present a curated set of recommendations for running a *successful* survey analysis. We hope this list provides practical insights that assist in producing meaningful and reliable results.
+The previous chapters in this book aimed to provide the technical skills and knowledge required for running survey analyses. This chapter builds upon the previously mentioned best practices to present a curated set of recommendations for running a successful survey analysis. We hope this list provides practical insights that assist in producing meaningful and reliable results.
 
 ## Follow the survey analysis process {#recs-survey-process}
 
@@ -75,7 +75,7 @@ The previous chapters in this book aimed to provide the technical skills and kno
 
 4. Within `summarize()`, specify variables to calculate, including means, totals, proportions, quantiles, and more
 
-The order of these steps matters in survey analysis. For example, if we need to subset the data, \index{Functions in srvyr!filter}we must use `filter()` on our data **after** creating the survey design. If we do this before the survey design is created, we may not be correctly accounting for the study design, resulting in inaccurate findings.\index{Survey analysis process|)}
+The order of these steps matters in survey analysis. For example, if we need to subset the data, \index{Functions in srvyr!filter}we must use `filter()` on our data after creating the survey design. If we do this before the survey design is created, we may not be correctly accounting for the study design, resulting in inaccurate findings.\index{Survey analysis process|)}
 
 Additionally, correctly identifying the survey design is one of the most important steps in survey analysis.  Knowing the type of sample design (e.g., clustered, stratified) helps ensure the underlying error structure is correctly calculated and weights are correctly used. Learning about complex design factors such as clustering, stratification, and weighting is foundational to complex survey analysis, and we recommend that all analysts review Chapter \@ref(c10-sample-designs-replicate-weights) before creating their first design object. Reviewing the documentation (see Chapter \@ref(c03-survey-data-documentation)) helps us understand what variables to use from the data.