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  <titleInfo>
    <title>Psychological statistics</title>
  </titleInfo>
  <name type="personal">
    <namePart>Noronha, Marian</namePart>
    <role>
      <roleTerm type="text">author.</roleTerm>
    </role>
  </name>
  <typeOfResource>text</typeOfResource>
  <genre authority="marc">abstract or summary</genre>
  <originInfo>
    <place>
      <placeTerm type="code" authority="marccountry">ii</placeTerm>
    </place>
    <dateIssued encoding="marc">2024</dateIssued>
    <issuance>monographic</issuance>
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  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
  <physicalDescription>
    <form authority="marcform">print</form>
    <extent>v, 225 pages : illustrations ; 24 cm.</extent>
  </physicalDescription>
  <abstract>"The book "Psychological Statistics" focuses on these aspects, providing a readable account of the role statistics play in the psychological sciences. By employing statistical methods, psychologists can uncover meaningful patterns, identify significant relationships, and draw reliable conclusions from their data. Statistics offer a systematic approach to analyzing information, enabling researchers to move beyond mere observations and make evidence-based claims. They help in testing hypotheses, comparing groups, and drawing inferences about the general population based on samples. The book serves as a comprehensive guide, emphasizing the significance of statistics in psychological research. By employing statistical methods, psychologists can enhance their understanding of human behavior and make informed decisions based on evidence and data." -- Back cover</abstract>
  <tableOfContents>Enhancing statistical inference in psychological research via prospective and retrospective design analysis -- A comparison of classical and modern measures of internal consistency -- Indies of effect existence and significance in the Bayesian framework -- Validation of subjective well-being measures using item response theory -- Detecting conditional dependence using flexible Bayesian latent class analysis -- Statistician, heal thyself: fighting Statophobia at the source -- Toward clarifying human information processing by analyzing big data: making criteria for individual traits in digital society -- Exploring the correlation between multiple latent variables and covariates in hierarchical data based on the multilevel multidimensional IRT model -- Modelling the information-psychological impact in social networks -- Machine learning in psychometrics and psychological research.</tableOfContents>
  <note type="statement of responsibility">edited by Marian Noronha.</note>
  <note>Includes bibliographical references and index.</note>
  <subject authority="lcsh">
    <topic>Psychometrics</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Psychology</topic>
    <topic>Statistical methods</topic>
  </subject>
  <classification authority="lcc">BF39 .P793 2024</classification>
  <classification authority="ddc" edition="23">150.15195 P959</classification>
  <identifier type="isbn">9788119365944</identifier>
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    <recordCreationDate encoding="marc">251223</recordCreationDate>
    <recordChangeDate encoding="iso8601">20260114111956.0</recordChangeDate>
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      <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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