Analysis of Covariance (ANCOVA) is used to describe analyses with a single response variable, continuous independent variables, and no factors. Such an analysis is also known as a regression. In fact, you can get almost identical results in SPSS by conducting this analysis using either the "Analyze > Regression > Linear"  menus or the "Analyze > General Linear Model (GLM) > Univariate" menus. 

In these methods you get slightly different output tables. Also, regression requires that a user dummy codes factors, while GLM handles dummy coding through the "contrasts" option. The linear regression command in SPSS also allows for variable entry in stages.

Analysis of Variance (ANOVA) tests three or more groups for mean differences based on a continuous (i.e. scale or interval) response variable (the independent variable). The term "factor" refers to the variable that distinguishes this group membership. Ethnic group, education categories, and Areas where people live (N, S, E, W) are examples of factors.

There are two main types of ANOVA: A "one-way" ANOVA compares levels (i.e. groups) of a single factor based on single continuous response variable (e.g. comparing mean satisfaction (a scale variable)  by 'Ethnic group') and a "two-way" ANOVA compares levels of two or more factors for mean differences on a single continuous response variable (e.g. comparing mean satisfaction by both 'Ethnic group ' and 'education category').

Anova is used for at least 3 continuous variables to compare their means (average scores) whereas Ancova is used where the outcome variable is continuous and one of the at least two predictors is categorical. Hope that help a bit. 

Analysis of Covariance (ANCOVA) is used to describe analyses with a single response variable, continuous independent variables, and no factors. Such an analysis is also known as a regression. In fact, you can get almost identical results in SPSS by conducting this analysis using either the "Analyze > Regression > Linear"  menus or the "Analyze > General Linear Model (GLM) > Univariate" menus. 

In these methods you get slightly different output tables. Also, regression requires that a user dummy codes factors, while GLM handles dummy coding through the "contrasts" option. The linear regression command in SPSS also allows for variable entry in stages.

Analysis of Variance (ANOVA) tests three or more groups for mean differences based on a continuous (i.e. scale or interval) response variable (the independent variable). The term "factor" refers to the variable that distinguishes this group membership. Ethnic group, education categories, and Areas where people live (N, S, E, W) are examples of factors.

There are two main types of ANOVA: A "one-way" ANOVA compares levels (i.e. groups) of a single factor based on single continuous response variable (e.g. comparing mean satisfaction (a scale variable)  by 'Ethnic group') and a "two-way" ANOVA compares levels of two or more factors for mean differences on a single continuous response variable (e.g. comparing mean satisfaction by both 'Ethnic group ' and 'education category').

An cova involve non-randomization of participarts usually find usefuf in  pretest-posttest control group quasi experimental design in other to partial out initial differences in term of entry background among the participants,but should in case  there is not such anova will be useful.Usually an ova is useful for intact classes where researcher use all the participants in the setting.