{"id":480,"date":"2023-08-11T14:04:12","date_gmt":"2023-08-11T18:04:12","guid":{"rendered":"http:\/\/www.horizon-research.com\/RAPID\/covid-k-12-appendix-copy\/"},"modified":"2023-09-26T15:58:39","modified_gmt":"2023-09-26T19:58:40","slug":"covid-2020-appendix-c","status":"publish","type":"page","link":"https:\/\/www.horizon-research.com\/RAPID\/covid-2020-appendix-c\/","title":{"rendered":"COVID 2020 Appendix C"},"content":{"rendered":"\n
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\n \n\t\t\t\t\tAPPENDIX C\t\t <\/span>\n\n\t\n \n\t\t\t\t\tResponding to a Global Pandemic:
The Role of K 12 Science Teachers\t\t <\/span>\n <\/h1>\n<\/div>\n\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t\n\t\t
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\n \n\t\t\t\t\tDescription of Reporting Variables\t\t <\/span>\n\n\t\n \n\t\t\t\t\tAPPENDIX C\t\t <\/span>\n <\/h1>\n<\/div>\n\n\n
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Grade Range<\/span><\/strong>
Teachers were classified by grade range (elementary, middle, and high) according to the information they provided about their teaching schedule. Elementary was defined as grades K\u20135 plus 6th grade self-contained; middle was defined as 6th grade non-self-contained and grades 7\u20138; high was defined as grades 9\u201312.<\/span><\/p>\n

Percentage of Students in School Eligible for Free\/Reduced-Price Lunch<\/span><\/strong>
Each teacher was classified into 1 of 4 categories based on the proportion of students in their school eligible for free\/reduced-price lunch (FRL). The categories were defined as quartiles within groups of schools serving the same grades\u2014e.g., schools with grades K\u20135, schools with grades 6\u20138 (see Table C-1).<\/span><\/p> <\/div>\n<\/div>\n\n

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Percentage of Students from Race\/Ethnicity Groups Historically Underrepresented in STEM in Class<\/span><\/strong>
Each teacher was classified into 1 of 4 categories based on the proportion of students in their school identified as being from underrepresented minority (URM) groups in STEM (i.e., American Indian or Alaskan Native, Black or African American, Hispanic or Latino, Native Hawaiian or Other Pacific Islander, multi-racial). As this proportion is similar in schools regardless of grades served, the categories were defined as quartiles across all classes (see Table C-2).<\/span><\/p> <\/div>\n<\/div>\n\n

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Type of Community<\/span><\/strong><\/p>\n

Each teacher was classified as belonging to 1 of 3 types of communities based on the location of their school:<\/span><\/p>\n

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  • Urban: Central city;<\/span><\/li>\n
  • Suburban: Area surrounding a central city, but still located within the counties constituting a Metropolitan Statistical Area (MSA); or<\/span><\/li>\n
  • Rural: Area outside any MSA.<\/span><\/li>\n<\/ul>\n

    Political Leaning of County<\/span><\/strong><\/p>\n

    Each teacher was classified into 1 of 2 groups based on which 2020 presidential candidate received the majority of votes in the county their school is located in.<\/span><\/p>\n

    Overview of Composites<\/span><\/strong><\/p>\n

    To facilitate the reporting of large amounts of survey data, and because individual questionnaire items are potentially unreliable, HRI used factor analysis to identify survey questions that could be combined into \u201ccomposites.\u201d Each composite represents an important construct related to COVID in science education.<\/span><\/p>\n

    Each composite is calculated by summing the responses to the items associated with that composite and then dividing by the total points possible. In order for the composites to be on a 100-point scale, the lowest response option on each scale was set to 0, and the others were adjusted accordingly; so, for example, an item with a scale ranging from 1 to 4 was re-coded to have a scale of 0 to 3. By doing this, someone who marks the lowest point on every item in a composite receives a composite score of 0 rather than some positive number. It also assures that 50 is the true mid-point. The denominator for each composite is determined by computing the maximum possible sum of responses for a series of items and dividing by 100; e.g., a 9-item composite where each item is on a scale of 0\u20133 would have a denominator of 0.27. Composite values were not computed for participants who responded to fewer than two-thirds of the items that form the composite.<\/span><\/p>\n

    The composites were derived through a multi-stage process. As a first step, to test whether the items intended to target the same underlying construct indeed showed similar response patterns, an exploratory factor analysis was conducted on a subset of the data. (The complete dataset was split randomly into two subsets to allow for independent exploratory and confirmatory factor\u00a0analyses.) Using Mplus version 8.1, several different factor solutions were produced, and scree plots, eigenvalues, and factor patterns were examined. Based on item fit and conceptual coherence, preliminary composite definitions were created. Next, the preliminary composite definitions were applied to a different subset of the data and a confirmatory factor analysis was performed, again using Mplus. Mplus provides one fit index to evaluate the model: the standardized root mean square residual (SRMR). The psychometric literature provides multiple criteria for judging acceptable model fit using this index, ranging from 0.05 to 0.10.9 The obtained values from final models are presented in the tables that follow, allowing the reader to apply their preferred criteria for evaluating fit. Lastly, to further aid in the assessment of the composites, Cronbach\u2019s coefficient alpha, a common measure of reliability, was calculated and is presented in the tables. An alpha of 0.6\u20130.8 is evidence of moderate reliability, and a value over 0.8 is considered evidence of strong reliability.<\/span><\/span><\/p>\n

    Definitions of Teacher Composites<\/span><\/strong><\/p>\n

    Composite definitions for the science, mathematics, and computer science teacher questionnaire are presented below along with the item numbers from the respective questionnaires. Composites that are identical for the two subjects are presented in the same table; composites unique to a subject are presented in separate tables.<\/span><\/p>\n

    Sources of Information About COVID<\/span><\/strong><\/p>\n

    These composites estimate the extent to which teachers used various sources of information about coronavirus\/COVID-19, whether for instruction or for personal use.<\/span><\/p> <\/div>\n<\/div>\n\n

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    Teaching About COVID<\/span><\/strong>
    These composites estimate the extent to which teachers used instructional activities to address COVID and the extent to which they covered certain COVID topics.<\/span><\/p> <\/div>\n<\/div>\n\n

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    \n\t\t\t\t\t\t\t\t\tTable C-13\t\t\t\t\t\t\t<\/h3>\n\t \n\t \t<\/div>\n\t\n \t<\/div>\n\n
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    Teacher Decision Making<\/span><\/strong><\/p>\n

    These composites estimate the extent to which various factors influenced whether teachers addressed COVID in their instruction.<\/span><\/p> <\/div>\n<\/div>\n\n

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    AppendixResponding to a Global Pandemic: The Role of K 12 Science Teachers APPENDIX Appendix A: Teaching Science During a Pandemic Teacher Questionairre Teaching Science During a Pandemic Teacher Questionairre Appendix B:\u00a0Sample Demographics Appendix C: Description of Reporting Variables Table C-1: Elementary Grades Table C-2: Middle School Teachers Table C-3: High School Acknowledgements\u00a0 This report is […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"class_list":["post-480","page","type-page","status-publish","hentry","has-post-title","has-post-date","has-post-category","has-post-tag","has-post-comment","has-post-author",""],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.horizon-research.com\/RAPID\/wp-json\/wp\/v2\/pages\/480","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.horizon-research.com\/RAPID\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.horizon-research.com\/RAPID\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.horizon-research.com\/RAPID\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.horizon-research.com\/RAPID\/wp-json\/wp\/v2\/comments?post=480"}],"version-history":[{"count":11,"href":"https:\/\/www.horizon-research.com\/RAPID\/wp-json\/wp\/v2\/pages\/480\/revisions"}],"predecessor-version":[{"id":1238,"href":"https:\/\/www.horizon-research.com\/RAPID\/wp-json\/wp\/v2\/pages\/480\/revisions\/1238"}],"wp:attachment":[{"href":"https:\/\/www.horizon-research.com\/RAPID\/wp-json\/wp\/v2\/media?parent=480"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}