{"id":97194,"date":"2003-04-16T00:00:00","date_gmt":"2003-04-16T05:00:00","guid":{"rendered":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/2003\/04\/16\/appendix-a-9\/"},"modified":"2024-04-14T04:14:25","modified_gmt":"2024-04-14T09:14:25","slug":"appendix-a-9","status":"publish","type":"post","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/appendix-a-9\/","title":{"rendered":"Appendix A"},"content":{"rendered":"<h3 data-is-section=\"true\" data-wp-context=\"{&quot;id&quot;:&quot;factor-analysis&quot;}\" data-wp-interactive=\"{&quot;namespace&quot;:&quot;prc-block\\\/table-of-contents&quot;}\" id=\"factor-analysis\" class=\"wp-block-heading\">Factor Analysis<\/h3>\n\n<p class=\"wp-block-paragraph\">Factor analysis is also known as latent variable analysis. It is a statistical technique aimed at answering the question: What are the underlying and unobserved factors that may explain \u2013 and, importantly, summarize \u2013 complex phenomenon? A classic use of factor analysis is to determine where people fall on the political spectrum.\u00a0 One cannot observe directly whether someone is liberal or conservative, but through a series of questions about how people behave and what their attitudes are, factor analysis permits a statistician to use observed variables (does a person support affirmative action, vote for Democrats, favor funding for social programs?) to explain an unobserved variables (she\u2019s a liberal or conservative). <\/p>\n\n<p class=\"wp-block-paragraph\">Factor analysis is useful for the Pew Internet Project\u2019s March-May 2002 survey.\u00a0 There are a wide range of questions about who people are and what they do (online and offline), but we have few preconceived notions, and little theory, about how individuals\u2019 characteristics may influence the decision to obtain Internet access.<\/p>\n\n<p class=\"wp-block-paragraph\">A number of factors grouped together in statistically meaningful and intuitive ways.\u00a0 The following list consists of the Project\u2019s labels for the grouping and the variables from the survey that define the labels:<\/p>\n\n<ul class=\"wp-block-list\">\n<li> <b>Personal Time<\/b> is made up of those who said they were satisfied with the time they spend with friends, family, on their hobbies, or for relaxation. <\/li>\n<li> <b>Social Network<\/b> consists of respondents who say they often (i.e., \u201cevery day\u201d or \u201ca few times a week\u201d) visit with family or friends, dine with family or friends, or call family or friends just to talk.\u00a0 <\/li>\n<li> <b>Social Capital<\/b> captures traditional measures of social capital such as whether a person belongs to a community group or whether a person belongs to a social club.<\/li>\n<li> <b>Other Groups<\/b>: although only a small share of our respondents (about 6%) said they belong to \u201cother\u201d groups, it was a distinct category.\u00a0 Those who said they belong to \u201cother\u201d groups classified themselves as group members, but not in any of the groups on which they were prompted, namely a community group, social club, youth group, a church group, or local sports league.<\/li>\n<li> <b>Church Goers<\/b>: those who belong to and attend church often.<\/li>\n<li> <b>Social Contentment<\/b> is made up of people who think most people are fair, can be trusted, and who have people to turn to for support.\u00a0 Whites also group in this factor.<\/li>\n<li> <b>Internet\/Computer users<\/b>: those people with online access and who identify themselves as computer users.<\/li>\n<li> <b>Extrovert<\/b> captures respondents who describe themselves as outgoing, talkative, and assertive.<\/li>\n<li> <b>Media Use:<\/b> captures respondents who, on a typical day, watch any TV, watch TV news, or read a newspaper.<\/li>\n<\/ul>\n\n<h3 data-is-section=\"true\" data-wp-context=\"{&quot;id&quot;:&quot;regression-analysis&quot;}\" data-wp-interactive=\"{&quot;namespace&quot;:&quot;prc-block\\\/table-of-contents&quot;}\" id=\"regression-analysis\" class=\"wp-block-heading\">Regression Analysis<\/h3>\n\n<p class=\"wp-block-paragraph\">The next step is a regression model that seeks to explain what causes people to adopt the Internet. The groupings that the factor analysis yielded on social and personal traits were included, as well as those relating to media use and technology traits.\u00a0 Demographic variables round out the types of variables included in each specification.\u00a0 <\/p>\n\n<p class=\"wp-block-paragraph\">Three models are reported in order to see how robust estimates are to the inclusion or exclusion of different variables.\u00a0 Model I includes all variables except \u201csocial contentment\u201d; instead the dichotomous variables for trust, support, and satisfaction with the country\u2019s direction are included. Model II substitutes the \u201csocial contentment\u201d variable for those variables.\u00a0 The variable for \u201cwhites\u201d is excluded here, as it groups with \u201csocial contentment\u201d.\u00a0 Finally, Model III excludes the variables on personal technology use (i.e., cell phones and personal digital assistants). \u00a0As discussed more fully below, the causal relationship between these variables and Internet use may run both ways, making it sensible from an econometric perspective to exclude them.\u00a0 <\/p>\n\n<h3 data-is-section=\"true\" data-wp-context=\"{&quot;id&quot;:&quot;interpreting-results&quot;}\" data-wp-interactive=\"{&quot;namespace&quot;:&quot;prc-block\\\/table-of-contents&quot;}\" id=\"interpreting-results\" class=\"wp-block-heading\">Interpreting Results<\/h3>\n\n<p class=\"wp-block-paragraph\">In interpreting the following\u00a0table, an odds ratio greater than one means that a user having the behavioral characteristic associated with that variable has a greater likelihood of having Internet access.\u00a0 Variables with asterisks have statistical significance; those without asterisks lack explanatory power.\u00a0 The odds ratio also allows us to compare the magnitude of the independent effects. For example, being a student is the strongest predictor of whether one goes online, followed by being a college graduate.<\/p>\n\n<p class=\"wp-block-paragraph\"><figure><img loading=\"lazy\" decoding=\"async\" width=\"323\" height=\"769\" alt=\"Table 1\" src=\"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/wp-content\/uploads\/sites\/9\/media\/C8A12B0AFD4048DB9195DFE84A52DE3B.jpg\" class=\"aligncenter\"><\/figure>\n<\/p>\n\n<p class=\"wp-block-paragraph\">Finally, the \u201cpercent concordant\u201d is a measure of how successfully the models predicts whether respondent go online.\u00a0 We know from the data which people go online; running the data through the models predicts correctly who goes online from between 77% and 79% of the time.\u00a0 In other words, that is nearly 30% better than flipping a coin.\u00a0 By the standards of this kind of regression model (a logistic regression), this is quite good.<\/p>\n\n<h3 data-is-section=\"true\" data-wp-context=\"{&quot;id&quot;:&quot;result-discussion&quot;}\" data-wp-interactive=\"{&quot;namespace&quot;:&quot;prc-block\\\/table-of-contents&quot;}\" id=\"result-discussion\" class=\"wp-block-heading\">Result: Discussion<\/h3>\n\n<p class=\"wp-block-paragraph\">In many ways, demography is destiny when it comes to predicting who will go online.\u00a0 Having a college degree, being a student, being white, being employed, and having a comfortable income each independently predict Internet use.\u00a0 Notably, gender is not a significant factor.\u00a0 As for race, being white is a strong predictor of whether one is online (Model I), controlling for all the other demographic variables in the model.\u00a0 When the model was run with blacks and Hispanics as the race variable (Model III), being black or Hispanic was a negative predictor of online access.\u00a0 Since being white groups with social contentment, the fact that social contentment is positive and significant in Model III, along with the presence of other racial categories in that model, is strong evidence that being white is a strong influence to going online. In sum, race matters; holding all other things constant, blacks and Hispanics are less likely to go online than whites.<\/p>\n\n<p class=\"wp-block-paragraph\">The other variables yield a couple of insights.\u00a0 Those whose worlds seem to be close around them are less likely to go online.\u00a0 People who belong to a community group or social club (i.e., those with traditional measures of social capital) are less likely to be online.[11.numoffset=&#8221;11&#8243; It is notable that for those respondents who belong to \u201cother\u201d groups (only about 6% of the sample) group membership is a positive predictor of online access. Since the \u201cother\u201d groups are unspecified, it would be well worth exploring specifically what kinds of group activities may be associated with Internet use.]\u00a0 Those with an active and immediate social network (i.e., those who frequently visit, talk, or dine with friends and family) are also less likely to go online.\u00a0 In slight contrast, those who are satisfied with the amount of time they can devote to family, friends, hobbies, and relaxation are more likely to be online.\u00a0 However, the size of this variable\u2019s predictive power is small and it is significant in only one model.\u00a0 In sum, it seems that the physical proximity of people and groups that matter to these people leaves little room (or need) for the Internet.<\/p>\n\n<p class=\"wp-block-paragraph\">People who exhibit a positive and outward orientation toward the world are more likely to be Internet users.\u00a0 Those who feel they have a lot of control over their lives, and who are also satisfied with the direction in which the United States is heading are more likely to go online than those who do not feel that way.\u00a0 The variable \u201csocial contentment\u201d reflects a grouping of people who think other people are fair, can be trusted, have others to turn to for support, and are white.\u00a0 That variable is significant in two models, and remains significant when the \u201cwhite\u201d variable is included.\u00a0 Since econometrically one would expect including both \u201csocial contentment\u201d (which partially captures race) and the race variable for white Americans to lessen the significance of each, this suggests that race <i>and<\/i> notions of social contentment are strongly related to Internet adoption.\u00a0 Finally, media use \u2013 those who watch TV news, read the newspaper, and regularly watch TV and arguably an indicator of an outward orientation \u2013 is also a positive predictor of Internet use.<\/p>\n\n<p class=\"wp-block-paragraph\">Of course, it is possible to have both an outward orientation toward the world, and a \u201cclose in\u201d social universe (as measured by social capital and nearby social networks).\u00a0 According to the model, if you are such a person, the odds are in favor of you being online.\u00a0 In other words, a person\u2019s outward orientation would outweigh a \u201cclose in\u201d social universe and mean that a person possessing both characteristics is more likely than not to be online.<\/p>\n\n<p class=\"wp-block-paragraph\">As for cost, the monthly cost of Internet access does not appear to have much to do with the decision to be online; in no specification was the cost a significant predictor of whether a person goes online.\u00a0 Finally and unsurprisingly, having technology is associated with Internet use.\u00a0 Those who have cell phones or personal digital assistants are likely to use the Internet.\u00a0 <\/p>\n\n<p class=\"wp-block-paragraph\">Including personal technologies (cell phone and PDAs) in the models raise the issue of causality.\u00a0 Having a cell phone may not <i>cause<\/i> one to obtain Internet access, but rather having several personal technologies is part of the same related process of being wired (e.g., with the Internet, a personal computer, a cell phone, etc.).\u00a0 Econometrically, this would bias the estimates in the models.\u00a0 Therefore, Model III excludes those variables.\u00a0 The predictive power of the model declines only slightly, and the signs, significance, and magnitude of the remaining parameter estimates remain the about same, with the \u201ccollege graduate\u201d and \u201cstudent\u201d variables picking up additional predictive power.<\/p>\n\n<p class=\"wp-block-paragraph\">The three models, then, portray a consistent picture; demographic characteristics (education, income, race, and others) are the strongest predictors of whether people use the Internet.\u00a0 People exhibiting a strong degree of social contentment\u2014whether measured by the \u201csocial contentment\u201d variable as defined above or by saying they have control of their lives, trust in others, and people to turn to for help\u2014are more likely to be online.\u00a0 Those who seem to have their social life very much nearby\u2014those who belong to a community group or social club and those who often visit with, talk to, or dine with family and friends\u2014are less likely to be online.\u00a0 <\/p>\n\n<p class=\"wp-block-paragraph\"> <i>Appendix A was written by Dr. John Horrigan, the senior research specialist at the Pew Internet &amp; American Life Project.<\/i> <\/p>","protected":false},"excerpt":{"rendered":"<p>Factor Analysis Factor analysis is also known as latent variable analysis. It is a statistical technique aimed at answering the question: What are the underlying and unobserved factors that may explain \u2013 and, importantly, summarize \u2013 complex phenomenon? A classic use of factor analysis is to determine where people fall on the political spectrum.\u00a0 One [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"sub_headline":"","sub_title":"","_crdt_document":"","_prc_public_revisions":[],"_ppp_expiration_hours":0,"_ppp_enabled":false,"ai_generated_summary":"","relatedPosts":[],"reportMaterials":[],"multiSectionReport":[],"package_parts__enabled":false,"package_parts":[],"_prc_fork_parent":0,"_prc_fork_status":"","_prc_active_fork":0,"datacite_doi":"","datacite_doi_citation":"","_prc_seo_qr_attachment_id":0,"spoken_article_player_enabled":true,"bylines":[],"acknowledgements":[],"displayBylines":true,"footnotes":"","prc_watchers":[]},"categories":[],"tags":[],"bylines":[],"collection":[],"datasets":[],"level_of_effort":[],"primary_audience":[],"information_type":[],"_post_visibility":[],"formats":[458],"_fund_pool":[],"languages":[],"regions-countries":[],"research-teams":[526],"workflow-status":[],"class_list":["post-97194","post","type-post","status-publish","format-standard","hentry","formats-report","research-teams-internet"],"label":false,"post_parent":97810,"word_count":1663,"canonical_url":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/appendix-a-9\/","art_direction":false,"_embeds":[],"watchers":[],"table_of_contents":[{"id":97810,"title":"The Ever-Shifting Internet Population: A new look at Internet access and the digital divide","slug":"the-ever-shifting-internet-population-a-new-look-at-internet-access-and-the-digital-divide","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/the-ever-shifting-internet-population-a-new-look-at-internet-access-and-the-digital-divide\/","is_active":false},{"id":97814,"title":"Part 1. Who\u2019s not online","slug":"part-1-whos-not-online","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/part-1-whos-not-online\/","is_active":false},{"id":97819,"title":"Part 2. Why non-users do not go online","slug":"part-2-why-non-users-do-not-go-online","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/part-2-why-non-users-do-not-go-online\/","is_active":false},{"id":97269,"title":"Part 3. What non-users think about the online world","slug":"part-3-what-non-users-think-about-the-online-world","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/part-3-what-non-users-think-about-the-online-world\/","is_active":false},{"id":97276,"title":"Part 4. The intentions of non-users","slug":"part-4-the-intentions-of-non-users","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/part-4-the-intentions-of-non-users\/","is_active":false},{"id":97281,"title":"Part 5. A new understanding of Internet use","slug":"part-5-a-new-understanding-of-internet-use","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/part-5-a-new-understanding-of-internet-use\/","is_active":false},{"id":97286,"title":"Part 6. Other social factors that relate to being offline","slug":"part-6-other-social-factors-that-relate-to-being-offline","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/part-6-other-social-factors-that-relate-to-being-offline\/","is_active":false},{"id":97292,"title":"Part 7. The Disabled: A Special Analysis","slug":"part-7-the-disabled-a-special-analysis","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/part-7-the-disabled-a-special-analysis\/","is_active":false},{"id":97299,"title":"Part 8. Conclusions","slug":"part-8-conclusions","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/part-8-conclusions\/","is_active":false},{"id":97185,"title":"Acknowledgments","slug":"acknowledgments-30-3","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/acknowledgments-30-3\/","is_active":false},{"id":97188,"title":"Methodology","slug":"methodology-143-2","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/methodology-143-2\/","is_active":false},{"id":97194,"title":"Appendix A","slug":"appendix-a-9","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/appendix-a-9\/","is_active":true},{"id":97199,"title":"Appendix B","slug":"appendix-b-5","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/appendix-b-5\/","is_active":false},{"id":97206,"title":"Bibliography","slug":"bibliography","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/bibliography\/","is_active":false}],"report_materials":"","report_pagination":{"current_post":{"id":97194,"title":"Appendix A","slug":"appendix-a-9","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/appendix-a-9\/","is_active":true,"page_num":12},"next_post":{"id":97199,"title":"Appendix B","slug":"appendix-b-5","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/appendix-b-5\/","is_active":false,"page_num":13},"previous_post":{"id":97188,"title":"Methodology","slug":"methodology-143-2","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/methodology-143-2\/","is_active":false,"page_num":11},"pagination_items":[{"id":97810,"title":"The Ever-Shifting Internet Population: A new look at Internet access and the digital divide","slug":"the-ever-shifting-internet-population-a-new-look-at-internet-access-and-the-digital-divide","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/the-ever-shifting-internet-population-a-new-look-at-internet-access-and-the-digital-divide\/","is_active":false,"page_num":1},{"id":97814,"title":"Part 1. Who\u2019s not online","slug":"part-1-whos-not-online","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/part-1-whos-not-online\/","is_active":false,"page_num":2},{"id":97819,"title":"Part 2. Why non-users do not go online","slug":"part-2-why-non-users-do-not-go-online","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/part-2-why-non-users-do-not-go-online\/","is_active":false,"page_num":3},{"id":97269,"title":"Part 3. What non-users think about the online world","slug":"part-3-what-non-users-think-about-the-online-world","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/part-3-what-non-users-think-about-the-online-world\/","is_active":false,"page_num":4},{"id":97276,"title":"Part 4. The intentions of non-users","slug":"part-4-the-intentions-of-non-users","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/part-4-the-intentions-of-non-users\/","is_active":false,"page_num":5},{"id":97281,"title":"Part 5. A new understanding of Internet use","slug":"part-5-a-new-understanding-of-internet-use","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/part-5-a-new-understanding-of-internet-use\/","is_active":false,"page_num":6},{"id":97286,"title":"Part 6. Other social factors that relate to being offline","slug":"part-6-other-social-factors-that-relate-to-being-offline","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/part-6-other-social-factors-that-relate-to-being-offline\/","is_active":false,"page_num":7},{"id":97292,"title":"Part 7. The Disabled: A Special Analysis","slug":"part-7-the-disabled-a-special-analysis","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/part-7-the-disabled-a-special-analysis\/","is_active":false,"page_num":8},{"id":97299,"title":"Part 8. Conclusions","slug":"part-8-conclusions","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/part-8-conclusions\/","is_active":false,"page_num":9},{"id":97185,"title":"Acknowledgments","slug":"acknowledgments-30-3","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/acknowledgments-30-3\/","is_active":false,"page_num":10},{"id":97188,"title":"Methodology","slug":"methodology-143-2","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/methodology-143-2\/","is_active":false,"page_num":11},{"id":97194,"title":"Appendix A","slug":"appendix-a-9","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/appendix-a-9\/","is_active":true,"page_num":12},{"id":97199,"title":"Appendix B","slug":"appendix-b-5","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/appendix-b-5\/","is_active":false,"page_num":13},{"id":97206,"title":"Bibliography","slug":"bibliography","link":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/internet\/2003\/04\/16\/bibliography\/","is_active":false,"page_num":14}]},"parent_info":{"parent_title":"The Ever-Shifting Internet Population: A new look at Internet access and the digital divide","parent_id":97810},"materialsOrdered":[],"chaptersOrdered":[],"partsOrdered":[],"partsEnabled":false,"datacite_doi":"","prc_seo_data":{"title":"Appendix A","description":"Factor Analysis Factor analysis is also known as latent variable analysis. It is a statistical technique aimed at answering the question: What are the underlying and unobserved factors that may&hellip;","og_title":"Appendix A","og_description":"","schema_type":"Article","noindex":false,"canonical_url":"","primary_terms":[],"custom_schema":[],"og_image":0,"indexnow_submitted_at":null,"gsc_index_status":null},"prepublish_checks":{"prc-image-alt-text":{"status":"complete","message":"No image blocks in content.","data":null},"prc-about-this-research":{"status":"incomplete","message":"Add an \"About this research\" details block.","data":null},"prc-paragraph-count":{"status":"complete","message":"Found 15 paragraphs.","data":{"count":15}},"prc-internal-link":{"status":"incomplete","message":"Add at least one internal link.","data":{"count":0}}},"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"relatedPostsOrdered":[],"bylinesOrdered":[],"acknowledgementsOrdered":[],"_links":{"self":[{"href":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/wp-json\/wp\/v2\/posts\/97194","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/wp-json\/wp\/v2\/comments?post=97194"}],"version-history":[{"count":2,"href":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/wp-json\/wp\/v2\/posts\/97194\/revisions"}],"predecessor-version":[{"id":133954,"href":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/wp-json\/wp\/v2\/posts\/97194\/revisions\/133954"}],"wp:attachment":[{"href":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/wp-json\/wp\/v2\/media?parent=97194"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/wp-json\/wp\/v2\/categories?post=97194"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/wp-json\/wp\/v2\/tags?post=97194"},{"taxonomy":"bylines","embeddable":true,"href":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/wp-json\/wp\/v2\/bylines?post=97194"},{"taxonomy":"collection","embeddable":true,"href":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/wp-json\/wp\/v2\/collection?post=97194"},{"taxonomy":"datasets","embeddable":true,"href":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/wp-json\/wp\/v2\/datasets?post=97194"},{"taxonomy":"level_of_effort","embeddable":true,"href":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/wp-json\/wp\/v2\/level_of_effort?post=97194"},{"taxonomy":"primary_audience","embeddable":true,"href":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/wp-json\/wp\/v2\/primary_audience?post=97194"},{"taxonomy":"information_type","embeddable":true,"href":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/wp-json\/wp\/v2\/information_type?post=97194"},{"taxonomy":"_post_visibility","embeddable":true,"href":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/wp-json\/wp\/v2\/_post_visibility?post=97194"},{"taxonomy":"formats","embeddable":true,"href":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/wp-json\/wp\/v2\/formats?post=97194"},{"taxonomy":"_fund_pool","embeddable":true,"href":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/wp-json\/wp\/v2\/_fund_pool?post=97194"},{"taxonomy":"languages","embeddable":true,"href":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/wp-json\/wp\/v2\/languages?post=97194"},{"taxonomy":"regions-countries","embeddable":true,"href":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/wp-json\/wp\/v2\/regions-countries?post=97194"},{"taxonomy":"research-teams","embeddable":true,"href":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/wp-json\/wp\/v2\/research-teams?post=97194"},{"taxonomy":"workflow-status","embeddable":true,"href":"https:\/\/alpha.pewresearch.org\/pewresearch-org\/wp-json\/wp\/v2\/workflow-status?post=97194"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}