Spss amos v24

Manufactured by IBM

SPSS AMOS v24 is a software package for creating and analyzing structural equation models. It provides a graphical user interface for specifying, estimating, assessing, and presenting models to show hypothesized relationships among variables.

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Spss v 21 for windows, by IBM (1 mentions) Spss v23, by IBM (1 mentions)

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SPSS v24 (1992 citations) SPSS Amos (119 citations) SPSSTM (8 citations)

4 protocols using spss amos v24

Confirmatory Factor Analysis Protocol

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The participants' characteristics were descriptively analysed. The data generated from the participants’ responses were analysed using SPSS for Windows v21 and SPSS AMOS v24. Data were presented as measures of central tendency, dispersion, frequencies, and percentages, as well as indices of internal reliability and validity using a confirmatory factor analysis (CFA). Fit indices considered from the CFA were the normed chi-square (χ2/df)), Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Goodness of Fit Index (GFI), Incremental Fit Index (IFI), and the Root Mean Square Error of Approximation (RMSEA). The normed chi-square (χ²/df) below the cut-off value of 3.0³⁵ or 5.0³⁶ (link) is recommended. The CFI, TLI, IFI, and GFI values greater than .90 are acceptable fits, while values greater than .95 are most preferred. RMSEA values lower than .10 indicate an acceptable fit, while values lower than .05 indicate a good fit.³⁷ ^,³⁸

Chia T., Oyeniran O.I, & Iorfa S.K. (2021). Validation of the fear of the COVID-19 scale in Nigeria: Implications for public health practice. Journal of Taibah University Medical Sciences, 16(6), 929-934.

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Psychosocial Impact of Dental Aesthetics

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IBM SPSS AMOS v.24 and SPSS v.23 were used for data analysis. Subjects’ agreement to each item of the PIDAQ(A) was measured on a 5-point Likert scale ranging from 1 (never) to 5 (very strongly) [12 (link)]. The subscale score was computed by the sum of the scores of its items except for the DSC subscale, where the item scores were reversed before summing up. The overall PIDAQ(A) score was calculated by summing up the subscale scores. Higher PIDAQ scores indicate poorer psychosocial impacts related to dental aesthetics [12 (link)].

Alsanabani A.A., Yusof Z.Y., Wan Hassan W.N., Aldhorae K, & Alyamani H.A. (2021). Validity and Reliability of the Arabic Version of the Psychosocial Impact of Dental Aesthetics Questionnaire for Yemeni Adolescents. Children, 8(6), 448.

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Self-Care Behaviors and Social Capital

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The socio-demographic variables were analyzed using descriptive statistics (including percentage, frequency, mean and standard deviation). A one-sample t-test was used to compare the differences regarding the participants’ self-care levels. Using a structural model, the authors assessed the relationships between the social capital and the self-care behavior among the women residing in the marginal, middle and upper parts of the city. They also compared the intensity of the relationships between the research variables in different parts. The data were analyzed in SPSS Amos v24 using structural equation modeling (SEM).

Shiri-Mohammadabad H, & Afshani S.A. (2021). Women’s self-care behavior and its relationship with social capital in Yazd, Iran. BMC Women's Health, 21, 331.

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Validating Factor Structure of CPRS and DDS

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Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted to examine the factor structure of the CPRS and DDS. Participants were randomly split into two subsamples of roughly equal size. Subsample 1 (n = 1565) was used for EFA and Subsample 2 (n = 1544) for CFA, which was conducted using SPSS-AMOS (v.24). CFA results were evaluated using multiple indices: Chi-Square/degree of freedom ratio (χ2/df), the goodness of fit index (GFI), adjusted goodness of fit (AGFI), comparative fit index (CFI), incremental fit index (IFI), Tucker-Lewis fit index (TLI), root mean squared error of approximation (RMSEA), and standardized root mean square residual (SRMR) were used to assess the fit of the model to data (Tabachnick & Fidell, 2007 ). The statistics that represent a “good” fit are demonstrated by GFI, AGFI, CFI, IFI and TLI ≥ 0.95, RMSEA ≤0.08, SRMR ≤0.05, and χ2/df < 3 (Hu & Bentler, 1999 ; Kline, 2005 ). Pearson's correlation was run to explore the relationships between the variables. The SPSS macro PROCESS (Hayes, 2013 ) was used to perform mediation analyses. The bootstrapping method with 5000 resamples to estimate the 95% confidence intervals (CI) was subsequently conducted to demonstrate the significance of indirect effects.

Yıldırım M, & Güler A. (2020). Positivity explains how COVID-19 perceived risk increases death distress and reduces happiness. Personality and Individual Differences, 168, 110347.

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