Merge pull request #93 from gungorMetehan/patch-3

Update standard.qmd

Author: ben-domingue

standard.qmd

@@ -7,13 +7,13 @@ Data standards are an essential mechanism of increasing reusability and interope
 
 ## Key Elements
 
-The below three features are essential components of an IRW dataset (with some small exceptions to be explained below). Note that the typesetting of the data elements is consistent with that of the IRW.
+The following three features are essential components of an IRW dataset (with some small exceptions to be explained below). Note that the typesetting of the data elements is consistent with that of the IRW.
 
-- `id` This is a persistent identifer for the focal unit, i.e. *the thing being measured*. This will typically be a person but in some cases may be, for example, a word (when interest is in establishing some property of the word).
+- `id` This is a persistent identifier for the focal unit, i.e., *the thing being measured*. This will typically be a person but in some cases may be, for example, a word (when interest is in establishing some property of the word).
 
-- `item` This is a persistent identifier for *the probe being used to measure*. There are some cases wherein a unique item identifier cannot be identified; for more information on these cases see the discussion of trials below. Note that the identifer should be kept in a form that makes subsequent matching to item text as straightforward as possible (e.g., identifiers that make it challenging to match back to original items, when available, should be avoided).
+- `item` This is a persistent identifier for *the probe being used to measure*. There are some cases wherein a unique item identifier cannot be identified; for more information on these cases see the discussion of trials below. Note that the identifier should be kept in a form that makes subsequent matching to item text as straightforward as possible (e.g., identifiers that make it challenging to match back to original items, when available, should be avoided).
 
-- `resp` This is the item responses. It is coded as a numeric value that can be directly utilized in various psychometric models. Given its centrality, we make a few additional points:
+- `resp` These are the item responses. It is coded as a numeric value that can be directly utilized in various psychometric models. Given its centrality, we make a few additional points:
 
   + Response values that were imputed in the original data have been removed.
 
@@ -27,7 +27,7 @@ These elements are optional features of IRW datasets. When IRW datasets have the
 
 - `rt` The response time used to produce an item response is coded in seconds.
 
-- `date` The calendar time at which a response was produced is included. Coding of this variable is done in two ways. In some cases, there were only relative dates (e.g., 30 days into data collection). In that case, we convert to the number of seconds since the first piece of data in the dataset was collected). In cases where more exact information was given (e.g., 1:30PM 03/04/2008), we convert to [Unix time](https://www.unixtimestamp.com/) (i.e., seconds since JAN 01 1970. (UTC)).
+- `date` The calendar time at which a response was produced is included. Coding of this variable is done in two ways. In some cases, there were only relative dates (e.g., 30 days into data collection). In that case, we convert to the number of seconds since the first piece of data in the dataset was collected. In cases where more exact information was given (e.g., 1:30PM 03/04/2008), we convert to [Unix time](https://www.unixtimestamp.com/) (i.e., seconds since JAN 01 1970. (UTC)).
 
 
 - `qmatrix` When items are classified into a small number of skills (i.e., for the purposes of cognitive diagnostic modeling \cite{ravand2015cognitive}), we have included these item-level classifications. Note that the column headers will be `qmatrix1`, ..., `qmatrixN` when there are `N` skills.
@@ -42,13 +42,13 @@ These elements are optional features of IRW datasets. When IRW datasets have the
 
 - `itemcov_` Covariates that are invariant for the measurement probe. NOTE: This was implemented as of V16.1; data added before that may not be reverse-compatible with this standardization element.
 
-- `item_family`  An identifier for groups of items that potentially have family resemblances such that assumptions related to local independence may be violated. Examples include items with a testlet structure, items that have common features (e.g., 'how important is it that people wash their hands regularly?' vesrus 'how important is it that you wash your hands regularly'), or items that are clones of each other. Each element of a family of items will have a unique identifier (e.g., items in the first testlet have id `1`, items in the second testlet have id `2`, etc) and items that are not a member of a family will be `NA`. NOTE: This was implemented as of V11.25; data added before that may not be reverse-compatible with this standardization element.
+- `item_family`  An identifier for groups of items that potentially have family resemblances such that assumptions related to local independence may be violated. Examples include items with a testlet structure, items that have common features (e.g., 'how important is it that people wash their hands regularly?' versus 'how important is it that you wash your hands regularly'), or items that are clones of each other. Each element of a family of items will have a unique identifier (e.g., items in the first testlet have id `1`, items in the second testlet have id `2`, etc.) and items that are not a member of a family will be `NA`. NOTE: This was implemented as of V11.25; data added before that may not be reverse-compatible with this standardization element.
 
 ## Additional Considerations
 
 In some cases, we deviate from the above rules for specific reasons. We describe those here. Some of the below may be considered more experimental features of the IRW; more information about these data will be forthcoming.
 
-- Process data: In general, a single row of data in the IRW corresponds to a unque response from an individual to an item. In the context of ['process data'](https://link.springer.com/article/10.1007/s11336-022-09880-8), more information about a response is available. In such cases, responses have identifers that can be used to understand the sequence of actions that led to a given response. 
+- Process data: In general, a single row of data in the IRW corresponds to a unique response from an individual to an item. In the context of ['process data'](https://link.springer.com/article/10.1007/s11336-022-09880-8), more information about a response is available. In such cases, responses have identifiers that can be used to understand the sequence of actions that led to a given response. 
 
 - Trials: Many constructs in cognitive psychology are measured via repeated trials of similar tasks wherein the probes either do not vary or vary in terms of some quantifiable feature of the stimulus. In such cases, the `item` column is uninformative and the columns beginning `trial_` will contain information about the different trials.