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  • Meta-analyses in survey methodology : a systematic review
    Čehovin, Gregor ; Bošnjak, Michael ; Lozar Manfreda, Katja
    Meta-analytic techniques have become the standard methods for aggregating the results from thematically related studies in the behavioral, health, and economic sciences. To analyze the state of the ... art of using meta-analyses in survey methodology, previous meta-analyses are systematically identified and classified according to the thematic areas the analyses address. This is followed by identifying gaps in research (i.e., areas where there are few or no existing meta-analyses) and investigating potential avenues for future meta-analyses in this field. The findings are based on a systematic search of two bibliographic harvesters (together covering 265 bibliographic databases), which yielded 54 eligible manuscripts reporting 60 meta-analytic studies and 91 effect sizes. To identify the thematic areas, the effect sizes are structured according to seven categories of the total survey error (TSE). Characteristics of primary studies that potentially influence the variability of meta-analytic findings also are considered, such as the survey mode, questionnaire design, and sample characteristics. The results show that the thematic areas of these meta-analyses cover only two of the seven TSE categories: measurement and nonresponse error. Thematic areas in the remaining TSE categories are not covered, and the key practical implication is that gaps exist in current research. Regarding the methodology of existing meta-analyses in the survey methodology field, there are fundamental differences in reporting styles in terms of transparency and replicability, calling for a concerted effort to develop meta-analytic reporting standards for survey methodology.
    Source: Public opinion quarterly. - ISSN 0033-362X (Vol. 82, no. 4, Win. 2018, str. 641-660)
    Type of material - article, component part
    Publish date - 2018
    Language - english
    COBISS.SI-ID - 35940445

source: Public opinion quarterly. - ISSN 0033-362X (Vol. 82, no. 4, Win. 2018, str. 641-660)
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