${{\sigma }^2}_p=({\mathit{MS}}_p-{\mathit{MS}}_{\mathit{pi}}-{\mathit{MS}}_{\mathit{po}}+{\mathit{MS}}_{\mathit{pio}})∕n_i{n}_o$	person variance component
${{\sigma }^2}_i=({\mathit{MS}}_i-{\mathit{MS}}_{\mathit{pi}}-{\mathit{MS}}_{\mathit{io}}+{\mathit{MS}}_{\mathit{pio}})∕n_p{n}_o$	item variance component
${{\sigma }^2}_{\mathit{pi}}=({\mathit{MS}}_{\mathit{pi}}-{\mathit{MS}}_{\mathit{pio}})∕n_o$	occasion variance component
${{\sigma }^2}_{\mathit{pi}}=({\mathit{MS}}_{\mathit{pi}}-{\mathit{MS}}_{\mathit{pio}})∕n_o$	person x item variance component
${{\sigma }^2}_{\mathit{po}}=({\mathit{MS}}_{\mathit{po}}-{\mathit{MS}}_{\mathit{pio}})∕n_i$	person x occasion variance component
${{\sigma }^2}_{\mathit{io}}=({\mathit{MS}}_{\mathit{io}}-{\mathit{MS}}_{\mathit{pio}})∕n_p$	item x occasion variance component
${{\sigma }^2}_{\mathit{pio}}={\mathit{MS}}_{\mathit{pio}}$	residual variance component