Preface |
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xxi | |
Introduction |
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xxv | |
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Section I Food security policy analysis |
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1 Introduction to food security: concepts and measurement |
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3 | (25) |
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3 | (1) |
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Conceptual framework of food security |
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4 | (3) |
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Food security in the developed world |
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7 | (4) |
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Other policy issues in the United States |
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11 | (1) |
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Food security concerns in other countries |
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11 | (5) |
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Measurement of the determinants of food security |
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16 | (1) |
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16 | (1) |
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Measuring food availability |
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17 | (1) |
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17 | (1) |
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18 | (1) |
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Measuring food utilization |
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18 | (1) |
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Stability of availability |
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19 | (1) |
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Alternative approaches in measuring food security |
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19 | (2) |
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21 | (2) |
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A natural question is why is measuring food insecurity important for better program design in developing countries? |
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23 | (3) |
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26 | (2) |
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2 Implications of technological change, postharvest technology, and technology adoption for improved food security--application of t-statistic |
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28 | (2) |
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Review of selected studies |
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30 | (3) |
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Postharvest technology and implications for food security |
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33 | (3) |
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Food security issues and technology in the United States |
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36 | (2) |
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Biofuels--the Chinese experience |
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38 | (2) |
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US Farm Policy and food security--background and current issues |
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40 | (4) |
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CEO-5 and coping mechanisms for the future |
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44 | (1) |
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Empirical analysis--a basic univariate approach |
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45 | (1) |
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Data description and analysis |
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46 | (1) |
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Two measures of household food security are computed |
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47 | (2) |
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Consumption components of the food security Index |
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49 | (2) |
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51 | (1) |
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Threshold of food security by each individual component |
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52 | (1) |
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Tests for equality of variances |
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52 | (1) |
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Student's f-test for testing the equality of means |
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53 | (3) |
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56 | (2) |
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58 | (1) |
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Constructing the cutoff points for components of the food security Index |
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58 | (1) |
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58 | (1) |
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59 | (1) |
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59 | (1) |
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Independent sample t-test assuming unequal variances |
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60 | (3) |
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63 | (1) |
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64 | (4) |
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3 Effects of commercialization of agriculture (shift from traditional crop to cash crop) on food consumption and nutrition--application of chi-square statistic |
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68 | (2) |
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70 | (1) |
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What is commercialization? |
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70 | (4) |
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Review of selected studies |
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74 | (5) |
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Organic farms and commercialization in the United States |
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79 | (2) |
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Organic farming in a global context |
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81 | (1) |
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82 | (1) |
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Data description and analysis |
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83 | (1) |
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Descriptive analysis: cross-tabulation results |
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84 | (3) |
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87 | (2) |
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Chi-square tests using STATA |
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89 | (4) |
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Conclusion and policy implications |
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93 | (2) |
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95 | (1) |
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Pearson's chi-square (Χ2) test of independence |
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95 | (1) |
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Student's t-test versus Pearson's chi-square (Χ2) test |
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96 | (1) |
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Limitations of the chi-square procedure |
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96 | (1) |
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Descriptive analysis: cross-tabulation results |
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97 | (2) |
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99 | (2) |
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101 | (1) |
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102 | (3) |
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4 Effects of technology adoption and gender of household head: the issue, its importance in food security--application of Cramer's V and phi coefficient |
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105 | (2) |
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Review of selected studies |
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107 | (2) |
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Female farm operators in Kenya and Ethiopia: recent evidence |
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109 | (1) |
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Rights, norms, and institutions: beyond technology adoption |
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110 | (2) |
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Female farm operators in the United States |
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112 | (1) |
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Women in agriculture: the global scene |
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113 | (3) |
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Uganda's coffee market: a case study |
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116 | (2) |
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118 | (1) |
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Data description and analysis |
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119 | (1) |
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Descriptive analysis: cross-tabulation results |
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120 | (1) |
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121 | (2) |
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Conclusion and policy implications |
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123 | (1) |
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Section highlights: Covid-19, women's burden, and the digital divide |
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123 | (3) |
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126 | (2) |
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128 | (1) |
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Phi coefficient and Cramer's V |
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128 | (1) |
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128 | (3) |
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131 | (1) |
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131 | (4) |
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5 Changes in food consumption patterns: its importance to food security--application of one-way ANOVA |
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135 | (3) |
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Determinants of food consumption patterns and its importance to food security and nutritional status |
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138 | (1) |
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139 | (1) |
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Review of selected studies |
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140 | (1) |
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Food consumption patterns for developing countries |
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141 | (3) |
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Nutritional and economic outcomes |
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144 | (1) |
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Food consumption patterns in the United States |
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145 | (2) |
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Food consumption patterns in India and China |
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147 | (3) |
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Empirical analysis and main findings |
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150 | (1) |
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150 | (1) |
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150 | (1) |
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150 | (3) |
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153 | (5) |
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Conclusion and policy implications |
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158 | (1) |
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159 | (1) |
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Underlying assumptions in the ANOVA procedure |
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160 | (1) |
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Decomposition of total variation |
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160 | (1) |
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Number of degrees of freedom |
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161 | (1) |
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161 | (1) |
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Relation of F to 7-distribution |
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161 | (1) |
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162 | (1) |
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Fruit intakes per week for three income groups {Fit and F3) |
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162 | (2) |
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Compute mean square between and mean square within |
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164 | (1) |
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Compute the calculated value of F |
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164 | (1) |
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165 | (3) |
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168 | (2) |
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6 Impact of market access on food security--application of factor analysis |
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170 | (1) |
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Assessing the linkages of market reforms on food security and productivity |
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171 | (2) |
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Review of selected studies |
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173 | (2) |
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Food deserts in the United States |
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175 | (3) |
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Access, information, and food security in Africa |
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178 | (2) |
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The role of the informal sector and food security |
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180 | (1) |
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Food security issues in Middle East and North Africa |
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180 | (1) |
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Food insecurity in South Asia: case studies of India and Afghanistan |
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181 | (4) |
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185 | (1) |
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186 | (1) |
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Eigenvalues and eigenvectors |
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186 | (1) |
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Properties of eigenvalues |
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187 | (1) |
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Data description and methodology |
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188 | (1) |
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189 | (1) |
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189 | (1) |
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Yield/technology indicators |
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189 | (1) |
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189 | (1) |
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Household level characteristics |
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190 | (1) |
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Factor analysis by principal components |
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190 | (1) |
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Step 1 Computing the observed correlation matrix |
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190 | (2) |
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Step 2 Estimating the factors |
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192 | (1) |
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Principal components analysis |
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193 | (1) |
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193 | (1) |
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193 | (4) |
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Step 3 Making the factors easier to interpret: rotation procedure |
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197 | (1) |
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Varimax orthogonal rotation |
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197 | (1) |
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Step 4 Computing factor scores |
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198 | (3) |
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Principal components analysis in STATA |
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201 | (3) |
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Conclusion and policy implications |
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204 | (2) |
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206 | (1) |
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Factor analysis decision process |
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206 | (5) |
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211 | (1) |
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212 | (10) |
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Section II Nutrition policy analysis |
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7 Impact of maternal education and care on preschoolers' nutrition--application of two-way ANOVA |
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222 | (1) |
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Conceptual framework: linkages between maternal education, child care, and nutritional status of children |
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223 | (1) |
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223 | (1) |
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Conceptual and measurement issues on child care |
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224 | (1) |
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225 | (2) |
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Review of selected studies |
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227 | (2) |
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Maternal education and nutrition status in the United States |
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229 | (3) |
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Children's nutrition and maternal education in Africa |
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232 | (1) |
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232 | (1) |
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232 | (1) |
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Substantive findings from Asia and Latin America |
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233 | (1) |
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234 | (1) |
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234 | (1) |
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Cross-tabulation of weight for height with mothers' educational levels |
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235 | (2) |
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237 | (1) |
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Definition of main effect |
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237 | (1) |
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Partitioning sum of squares |
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237 | (4) |
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Interpreting the interaction effect and post hoc tests |
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241 | (1) |
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242 | (6) |
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248 | (1) |
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249 | (1) |
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Scoring system used to create the care index (Ruel et al., 1999) care Index by age group |
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249 | (1) |
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249 | (1) |
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250 | (3) |
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253 | (1) |
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254 | (5) |
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8 Indicators and causal factors of nutrition--application of correlation analysis |
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259 | (2) |
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Review of selected studies |
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261 | (3) |
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Food insecurity and nutrition in the United States |
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264 | (5) |
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Food insecurity in Brazil |
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269 | (1) |
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Global Monitoring Report on Nutrition and Millennium Development Coals |
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269 | (1) |
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Impact of food price spike and domestic violence in rural Bangladesh |
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270 | (1) |
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Malnutrition and chronic disease in India |
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271 | (1) |
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Malnutrition in Guatemala |
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272 | (1) |
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Empirical analysis and main findings |
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273 | (1) |
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Data description and methodology |
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274 | (1) |
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Concepts in correlation analysis |
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275 | (1) |
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Inference about population parameters in correlation |
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276 | (1) |
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277 | (1) |
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278 | (1) |
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Correlation analysis of the outcome variables |
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279 | (2) |
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Estimating correlation using STATA |
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281 | (2) |
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Conclusion and policy implications |
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283 | (3) |
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Estimating correlation using R |
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286 | (3) |
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289 | (1) |
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289 | (7) |
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9 Effects of individual, household, and community indicators on child's nutritional status--application of simple linear regression Introduction |
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296 | (40) |
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Conceptual framework and indicators of nutritional status |
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297 | (1) |
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Household utility maximization framework |
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297 | (2) |
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Core indicators of nutritional status |
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299 | (4) |
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Review of studies on the determinants of child nutritional status |
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303 | (4) |
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Child's nutritional status in the United States |
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307 | (1) |
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The economics of double burden |
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308 | (1) |
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AIDS and double burden in Africa |
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309 | (1) |
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Malnutrition and mortality in Pakistan and India |
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310 | (2) |
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Social participation as social capital, women empowerment, and nutrition in Peru |
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312 | (1) |
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Social capital and policy during the pandemic |
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313 | (3) |
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Empirical analysis and main findings |
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316 | (1) |
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316 | (2) |
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Incidence of stunting and wasting |
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318 | (1) |
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Normality tests and transformation of variables |
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319 | (2) |
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321 | (4) |
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Simple regression in STATA |
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325 | (2) |
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327 | (1) |
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328 | (2) |
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330 | (1) |
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331 | (5) |
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10 Maternal education and community characteristics as indicators of nutritional status of children--application of multivariate regression |
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336 | (1) |
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Selected studies on the role of maternal education and community characteristics on child nutritional status |
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337 | (5) |
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Community characteristics and Children's nutrition in the United States |
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342 | (3) |
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Community characteristics and child nutrition in Kenya |
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345 | (1) |
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Financial crisis and child nutrition in East Asia |
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345 | (1) |
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Double burden within mother--child pairs: Asian case |
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346 | (1) |
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347 | (3) |
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Data description and methodology |
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350 | (2) |
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Descriptive summary of independent variables |
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352 | (1) |
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353 | (1) |
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Step 1 Estimating the coefficients of the model |
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353 | (2) |
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Step 2 Examining how good the model predicts |
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355 | (2) |
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Step 3 Hypotheses Testing |
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357 | (5) |
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Step 4 Checking for violations of regression assumptions |
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362 | (6) |
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368 | (3) |
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371 | (1) |
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372 | (4) |
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376 | (6) |
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Section III Special topics on poverty, nutrition, and food policy analysis |
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11 Predicting child nutritional status using related socioeconomic variables--application of discriminant function analysis |
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382 | (2) |
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Conceptual framework: linkages between women's status and child nutrition |
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384 | (1) |
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Linkages between women's status and child nutrition |
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384 | (1) |
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Review of selected studies |
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385 | (1) |
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Direct linkage studies between women's status and children's nutritional status |
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385 | (3) |
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Indirect linkages between women's status and child's nutritional status |
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388 | (2) |
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US DA nutrition assistance programs: a case study from the United States |
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390 | (3) |
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Case studies of women's status and child nutritional status from Africa, Asia, and Latin America |
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393 | (1) |
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Food security and welfare in Africa: social customs, technology, and climate change |
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393 | (3) |
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Childhood undernutrition and climate change in Asia |
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396 | (1) |
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Adaptive strategies and sustainability lessons from Latin America |
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397 | (1) |
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Can garden plots save Russia? |
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398 | (1) |
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Empirical analysis and main findings |
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398 | (1) |
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Data description and analysis |
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399 | (2) |
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401 | (2) |
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Testing the assumptions underlying discriminant analysis model |
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403 | (1) |
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403 | (1) |
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Tests of equality of group means |
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404 | (1) |
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405 | (2) |
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Relative impact of the predictor variables on ZWHNEW |
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407 | (1) |
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Correlation between the predictor variables and discriminant function |
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408 | (1) |
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Classification statistics |
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409 | (1) |
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Classification function based on equal and unequal prior probabilities |
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410 | (2) |
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Canonical discriminant analysis using STATA |
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412 | (4) |
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416 | (1) |
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Technical appendix: discriminant analysis |
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417 | (1) |
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Discriminant analysis decision process |
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417 | (7) |
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Canonical discriminant analysis using R |
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424 | (2) |
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426 | (1) |
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426 | (8) |
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12 Measurement and determinants of poverty-- application of logistic regression models |
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434 | (1) |
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Dimensions and rationale for measuring poverty |
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435 | (1) |
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Defining and measuring poverty |
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435 | (1) |
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435 | (1) |
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436 | (1) |
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436 | (1) |
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Participatory poverty approach |
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437 | (2) |
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Rationale for measuring poverty |
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439 | (1) |
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Indicators in measuring poverty |
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439 | (1) |
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440 | (1) |
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440 | (2) |
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Construction of poverty lines using food energy intake and cost of basic needs approaches |
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442 | (1) |
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442 | (1) |
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Absolute and relative poverty |
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443 | (1) |
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Referencing and identification problems |
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443 | (1) |
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444 | (1) |
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Food energy intake method |
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444 | (2) |
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Cost of basic needs method |
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446 | (3) |
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New measures of poverty based on the engel curve |
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449 | (1) |
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449 | (5) |
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Selected review of studies on determinants of poverty |
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454 | (4) |
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Poverty and welfare in the United States |
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458 | (5) |
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Agriculture and poverty in Laos and Cambodia |
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463 | (2) |
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Financial crisis and poverty in the Russian Federation |
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465 | (1) |
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465 | (2) |
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Poverty in developing countries: China and India |
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467 | (1) |
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Determinants of poverty--binary logistic regression analysis |
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468 | (1) |
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Dichotomous logistic regression model |
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469 | (1) |
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An example with the Malawi dataset |
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469 | (1) |
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Expected determinants of household welfare |
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470 | (2) |
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472 | (1) |
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472 | (1) |
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472 | (1) |
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Hosmer--Lemeshow goodness-of-fit test |
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473 | (1) |
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Generalized coefficient of determination |
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474 | (1) |
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475 | (1) |
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Interpreting the logistic coefficients and discussion of results |
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476 | (2) |
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Estimating logistic regression models in STATA |
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478 | (1) |
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478 | (4) |
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482 | (3) |
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Conclusions and implications |
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485 | (1) |
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486 | (1) |
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Technical notes on logistic regression model |
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486 | (2) |
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Estimating logistic regression models in R |
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488 | (1) |
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489 | (1) |
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490 | (3) |
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13 Classifying households on food security and poverty dimensions--application of K-Means cluster analysis |
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493 | (2) |
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Food hardships and economic status in the United States |
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495 | (4) |
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Food security, economic crisis, and poverty in India |
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499 | (2) |
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Cluster analysis: various approaches |
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501 | (1) |
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Hierarchical clustering method |
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501 | (1) |
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Single linkage (nearest neighbor method) |
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502 | (1) |
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Complete linkage (farthest neighbor method) |
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502 | (1) |
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503 | (1) |
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503 | (1) |
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Review of selected studies using cluster analysis |
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503 | (3) |
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Empirical analysis: K-Means clustering |
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506 | (1) |
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507 | (1) |
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Initial partitions and optimum number of clusters |
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507 | (1) |
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Descriptive characteristics of the cluster of households |
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508 | (2) |
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510 | (3) |
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Cluster analysis in STATA |
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513 | (3) |
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Conclusion and implications |
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516 | (1) |
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517 | (2) |
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519 | (1) |
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519 | (4) |
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523 | (4) |
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14 Household care as a determinant of nutritional status--application of instrumental variable estimation |
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527 | (2) |
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Review of selected studies |
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529 | (5) |
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Federal nutrition programs and children's health in United States |
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534 | (2) |
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Parental unemployment and children's health in Germany |
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536 | (2) |
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Food security using the Gallup World Poll |
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538 | (1) |
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539 | (1) |
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Stage 1 Estimating Child-care practices |
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540 | (1) |
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Stage 2 Estimating the determinants of child health (weight-for-age Z-Scores) |
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541 | (2) |
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IV estimation using STATA |
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543 | (2) |
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545 | (1) |
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Instrumental variable estimation using R |
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546 | (1) |
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547 | (1) |
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548 | (4) |
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552 | (5) |
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15 Achieving an ideal diet--modeling with linear programming |
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557 | (2) |
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559 | (4) |
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563 | (2) |
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565 | (1) |
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Graphical solution approach |
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565 | (2) |
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Some qualifications about the optimum |
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567 | (1) |
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Using solver in excel to obtain an LP solution |
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568 | (1) |
|
Step 1 Setting the problem in excel |
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|
568 | (2) |
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Step 2 Solving the parameters of the model |
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|
570 | (1) |
|
Step 3 Deriving the results |
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|
570 | (2) |
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|
572 | (1) |
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|
573 | (2) |
|
16 Food and nutrition program evaluation |
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|
|
|
575 | (2) |
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|
577 | (1) |
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|
577 | (2) |
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|
579 | (4) |
|
|
583 | (2) |
|
Regression discontinuity design |
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|
585 | (2) |
|
Propensity score matching and pipeline comparisons |
|
|
587 | (1) |
|
Randomization and development policy: applying the methods |
|
|
588 | (3) |
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|
591 | (1) |
|
Section highlights: nobelprize worthy |
|
|
591 | (1) |
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|
592 | (2) |
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|
594 | (8) |
|
17 Multidimensional poverty and policy |
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|
|
Multidimensional child poverty and gender inequalities |
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|
602 | (1) |
|
Multidimensional energy poverty |
|
|
603 | (1) |
|
Financial exclusion and Multidimensional Poverty Index |
|
|
604 | (1) |
|
The Alkire--Foster method |
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|
604 | (5) |
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|
609 | (5) |
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|
614 | (5) |
|
Section IV Technical appendices |
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|
|
Appendix 1 Introduction to software access and use |
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|
619 | (2) |
|
Appendix 2 Software information |
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|
621 | (2) |
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Appendix 3 SPSS/PC+ environment and commands |
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|
623 | (10) |
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|
633 | (8) |
|
Appendix 5 SPSS programming basics |
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|
641 | (14) |
|
Appendix 6 STATA--a basic tutorial |
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|
655 | (8) |
|
Appendix 7 Anthropometric indicators--computation and use |
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|
663 | (6) |
|
Appendix 8 Elements of matrix algebra |
|
|
669 | (6) |
|
Appendix 9 Some preliminary statistical concepts |
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|
675 | (4) |
|
Appendix 10 Instrumental variable estimation |
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|
679 | (6) |
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Appendix 11 Statistical tables |
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|
685 | (10) |
References |
|
695 | (40) |
Index |
|
735 | |