make_metric: Calculate raw common metric values

In adamMaier/tntpmetrics: TNTP Common Metric Analysis and Preparation

Description

make_metric takes raw data containing all of the indicators, survey items, etc. needed to calculate a score on a common metric. It creates a new column/variable in your data with the value of the scored metric.

Usage

make_metric(data, metric, scaleusewarning = T)

Arguments

data	Data from a single timepoint. Used in tntpmetric_mean.
metric	Quoted name of the common metric. Options are "engagement", "belonging", "relevance", "assignments", "expectations", "expectations_old", tntpcore", or "ipg".
scaleusewarning	A logical (T/F) indicating whether function should generate a warning when not all values of a scale are used. For example, student survey data that only contains values of 1s and 2s could mean that data is on a 1-4 scale, when it should be on a 0-3 scale. When scaleusewarning = T, the function will warn you of this. This warning does not mean your data is wrong. For example, the Academic Ownership domain from TNTP CORE has 5 potential values: 1, 2, 3, 4, or 5. It's not uncommon to have data where teachers were never rated above a 4 on this domain. In this case, the printed warning can be ignored. Default if TRUE. If you are confident your data is on the right scale, you can suppress the warning by setting to TRUE.

Value

A data.frame identical to the original except with new columns/variables named as the input to metric with a cm_ prefix (e.g., cm_engagement, cm_ipg, etc.) that has the value of the scored metric. For metrics that have a specific cut-point above which scores designate something meaningful (e.g., expectations score of at least 12 represent "high expectations") another new variable is also created with a cm_binary_ prefix (e.g., cm_binary_expectations). This variable is logical (TRUE/FALSE) with values of true implying the row has, for example, high expectations. Currently, attributes of original data.frame (like groups) are not preserved.

Data and Variable Format

make_metric should be used with the raw metric data. Each row of data should represent a single rated outcome. For example, each row of data will be a single completed survey, a single rated assignment, a single classroom observation, etc. The data must have the components needed to score the construct. For example, data on student engagement should have variables corresponding to the four survey questions used to calculate engagement. Leave all items in their raw form - the functions automatically account for items that need to be reverse coded (if any). The only requirement is that the data contains the needed variables and that the variables are numeric (i.e., data values should be 0s and 1s, not 'No' and 'Yes'. This ensures that the common metrics are calculated correctly and consistently across projects. Each metric has its own set of needed variables that must be spelled exactly as shown below. They are:

engagement:

eng_like, eng_losttrack, eng_interest, eng_moreabout

belonging:

tch_problem, bel_ideas, bel_fitin, tch_interestedideas

relevance:

rel_asmuch, rel_future, rel_outside, rel_rightnow

expectations:

exp_fairtomaster, exp_oneyearenough, exp_allstudents, exp_appropriate

expectations_old:

exp_allstudents, exp_toochallenging, exp_oneyear, exp_different, exp_overburden, exp_began

tntpcore:

ec, ao, dl, cl

ipg:

form, grade_level, ca1_a, ca1_b, ca1_c, ca2_overall, ca3_overall, col. K-5 Literacy observations must also have rfs_overall. Science observations must also have ca1_d, ca1_e, ca1_f, and science_filter

assignments:

content, relevance, practice

Note that these are the NAMES of the variables needed in your data. It can be okay if some of these variables have NA values for specific rows. For example, K-5 Literacy observations on the IPG require either all of the Core Actions (ca1_a, ca1_b, ca1_c, ca2_overall, ca3_overall) and/or rfs_overall. If an observation has all the core actions it still needs a variable called rfs_overall, but the value can just be NA. See the vignette("analyzing_metrics") for more details. Note on Expectations. The items used to measure expectations shifted from a collection of six, mostly reverse-coded worded items to four positively worded items. Both expectations metrics are available, with the current 4-item expectations metric known as "expectations" and the older 6-item expectations metric known as "expectations_old". See the vignette("analyzing_metrics") for more details.

Examples

# Compute the engagement score for each collected survey
test_data <- make_metric(ss_data_initial, metric = "engagement")
head(test_data)

adamMaier/tntpmetrics documentation built on Feb. 1, 2022, 1:03 p.m.