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    Outliers and EFA Analysis

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    Hushnak Ali
    The document discusses exploratory factor analysis (EFA) techniques in SPSS, including assumptions that must be checked, such as sample size, normality, and outliers. It covers conducting EFA steps like factor extraction and rotation. The final section provides guidance on reporting EFA results in a research paper.

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    © All Rights Reserved
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    Outliers and EFA Analysis

    Uploaded by

    Hushnak Ali
    64 views17 pages
    The document discusses exploratory factor analysis (EFA) techniques in SPSS, including assumptions that must be checked, such as sample size, normality, and outliers. It covers conducting EFA steps like factor extraction and rotation. The final section provides guidance on reporting EFA results in a research paper.

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    The document discusses exploratory factor analysis (EFA) techniques in SPSS, including assumptions that must be checked, such as sample size, normality, and outliers. It covers conducting EFA steps like factor extraction and rotation. The final section provides guidance on reporting EFA results in a research paper.

    Copyright:

    © All Rights Reserved
    Download as PPTX, PDF, TXT or read online from Scribd
    Download as pptx, pdf, or txt
    64 views17 pages

    Outliers and EFA Analysis

    Uploaded by

    Hushnak Ali
    The document discusses exploratory factor analysis (EFA) techniques in SPSS, including assumptions that must be checked, such as sample size, normality, and outliers. It covers conducting EFA steps like factor extraction and rotation. The final section provides guidance on reporting EFA results in a research paper.

    Copyright:

    © All Rights Reserved
    Download as PPTX, PDF, TXT or read online from Scribd
    Download as pptx, pdf, or txt
    of 17

    Quantitative Research Techniques

    with SPSS
    Objectives

     Exploratory Factor Analysis (EFA)


     Introduction
     Assumptions of EFA
     Data Screening & Cleaning
     Missing values
     Aberrant values
     Normality & outliers
     KMO & Bartlet’s Tests
     No of factors to be retained
     Labeling the extracted factors
     Conducting EFA in SPSS
     Reporting results of EFA in a research paper

    2
    Factor Analysis: Introduction

     It’s a data reduction technique


     Terminology
     Factor & Item
     Exploratory Factor Analysis (EFA)
     Confirmatory Factor Analysis (CFA)

     Steps Involved in EFA


     Checking suitability of data
     Factor extraction
     Factor rotation and interpretation

    3
    Factor analysis
     This family of factor analytic techniques has a number of
    different uses. It is used extensively by researchers involved in
    the development and evaluation of tests and scales.

     The scale developer starts with a large number of individual


    scale items and questions and, by using factor analytic
    techniques, they can refine and reduce these items to form a
    smaller number of coherent subscales.

     Factor analysis can also be used to reduce a large number of


    related variables to a more manageable number, prior to using
    them in other analyses such as multiple regression or
    multivariate analysis of variance.

    4
    Factor analysis
     Types of factor analysis
     Exploratory and confirmatory
     Confirmatory factor analysis
     Exploratory factor analysis is often used in the early stages of
    research to gather information about (explore) the
    interrelation-ships among a set of variables.

     Confirmatory factor analysis, on the other hand, is a more


    complex set of techniques used later in the research process to
    test (confirm) specific hypotheses or theories concerning the
    structure underlying a set of variables.

    5
    Missing value analysis

    6
    Aberrant/abnormal values
     Aberrant values are those values that do not fall in the given response
    categories, and it might because of typing error during data entry (Tufféry &
    Riesco, 2011).

     Procedure

    7
    Multivariate outliers
     The outlier is referred as “a case with such an extreme value on one construct (a
    univariate outlier) or such a strange combination of the score on two or more
    constructs (multivariable outlier)” (B. Tabachnick & L. Fidell, 2007b, p.72).
     Procedure

    8
    Reversing negatively worded
    items
     In some scales the wording of particular items has been reversed to help
    prevent response bias.

    9
    Here we go…

    10
    11
    Steps Involved in EFA

     Checking Suitability of Data


     Variable type must be continuous
     Minimum sample size >= 150 or 5 cases per item
     Sufficient >= .30 inter items correlations
     KMO >= .60 & Bartlet’s Test p<.05
     Factor Extraction
     Kaiser’s Criterion (Eigen value >=1)
     Catell’s Scree test (select factors above the elbow)
     Factor Rotation & Interpretation

    12
    EFA
    Hands on Experience

    13
    Reporting Results of EFA in a Report

    14
    Reporting Results of EFA in a Report

    15
    Reporting Results of EFA in a Report

    16
    Checking the reliability
    of a scale
     Reliability refers the degree to which the items that make up the scale
    ‘hang together’. Are they all measuring the same underlying construct?
    One of the most commonly used indicators of internal consistency is
    Cronbach’s alpha coefficient.
     Procedure

    17

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