while the tests of basic motor abilities such as speed , maximum strength or endurance are well recognized , testing of complex motor abilities such as agility remains unresolved in the current literature .
the term agility itself has undergone changes , resulting in the current definition :  the ability to change speed and direction of a movement rapidly and accurately  ( jeffreys , 2011 ; sheppard et al . , 2014 ) , usually in response to a highly specific stimulus .
the specific stimuli include different situations during sport competitions that vary in intensity , with the condition of space - time uncertainty being met .
the term  agility  has evolved significantly , with the main factors determining agility also changing .
the most current model divides agility into cognitive , physical and technical factors ( young et al . , 2015 ) , instead of the perceptual one with decision - making factors and change - of - direction speed ( cods ) factors that were used in the previous model ( young et al . , 2002 ;
the result of this inconsistency created additional specific terms to describe specific parts of agility , such as reactive agility , quickness or reactiveness .
however , no quantitative description is currently available to describe and test agility itself or its partial factors
. models of main factors determining agility * factors evaluated by principal component analysis or factor analysis - exploratory approach . empirically accepted tests of agility have been screened to determine their relationships between each other and to specific subtests , which delineate any agility sub - factors .
l  agility test ( r = 0.73 ) and moderately with the reactive agility test ( r = 0.50 ) ( gabbett et al . , 2008 ) , and the factor of straight sprinting represented by the 36 m dash correlated with the  t  agility test ( r = 73 ) ( pauole et al . , 2000 ) . in these cases
, the authors determined whether the performance on one test was related to another , indicating that if an athlete performed well during one test , he may perform well in another test .
these types of findings resolve the specific relationships among agility sub - tests and are useful to explain the role of specific tests in sports training .
however , this approach does not offer an explanation of the structure of the main factors that determine agility ; for example , how does one explain how significantly the 36 m dash , 
l  agility test or reactive agility test predicts overall agility ? moreover , the choice of an agility test should clearly fit into an agility structure and not cross between its sub - factors with unknown factorial strengths ; therefore , it is currently not known whether five or ten tests are enough to estimate agility performance .
it may be advantageous to reduce the seemingly infinite number of agility tests when aiming to determine agility performance .
moreover , it may be possible to test independent sub - factors of agility that can effectively estimate agility performance .
the empirical aspect of agility testing is necessary as the choice of the test can differ with respect to the specificity of the sport discipline or performance level . since the relationships among individual agility tests are already known for numerous sports and athletic populations ( sheppard et al . , 2014 ;
young et al . , 2015 ) , quantifying to what extent the underlying sub - factors of agility ( i.e. , leg strength , change - of - direction speed , linear sprint speed , anthropometrics and other quantifiable variables ) directly impact agility performance could be useful . although uncommon within sport science literature , structural equation modeling ( sem ) is common within behavioral research ( maccallum and austin , 2000 ; mcneill et al . , 2006 ; marsh et al . ,
sem seeks to explain the relationships between independent and dependent variables , which can be either continuous or discrete ( schreiber et al . , 2006 ) . from this perspective
, sem is often understood as a combination of factor analysis and multiple regression analysis or path analysis ( hox and bechger , 1998 ; ullman , 2001 ; magiera et al .
therefore , sem is often used in situations when studies aim to empirically establish the structure of a certain behavioral domain without any evidence or information about relationships and their strengths ( kline , 2011 ) .
for example ,  intelligence  is a latent variable that does not have an observational or quantifiable result ; rather , it is inferred based on quantifiable data from the underpinning subfactors of intelligence that have observational outcomes based on previously established testing norms ( spatial , kinesthetic , musical , linguistic , etc . ) .
much like intelligence , agility is a latent variable with underlying sub - factors ( leg strength , linear sprint speed , reaction time , etc . ) .
therefore , the evidence and information about relationships between manifest and latent variables are important for modeling of agility structure and further development of valid and reliable tests . the models of agility s main underlying sub - factors ( jeffreys , 2011 ; sheppard et al . , 2014 ;
were specifically based on the empirical approach , while the explanation and application of the agility tests were derived mainly from the following : 1 ) validity and reliability of indicators that were considered criteria of agility performance ; 2 ) correlations revealed between these indicators and other manifest variables such as the  t  agility test , straight sprint , countermovement jump or skinfold thickness ( gabbett et al . , 2008 ;
therefore , the aim of this review was to determine which main factors could quantitatively determine agility performance . in an attempt to explain and describe the relationships between manifest and latent variables and distinguish them from the underlying factors of agility ,
different approaches were used , such as principal component analysis ( pca ) , factor analysis and sem .
the results provide an overview of agility structure modeling , shed light on scientifically unresolved portions of defined agility structures and discuss directions for future research .
this study utilized the preferred reporting items for systematic reviews and meta - analyses ( prisma ) ( moher et al . , 2010 )
the database search was conducted in may 2016 , and the web of science , pubmed and scopus databases were screened from 1976 to 2015 .
the title and abstract screening was focused on studies that included tests of agility or cods , testing agility or cods , relationships between agility and cognition , the role of agility in team sport games , agility or cods in relation to sport performance and any factors influencing agility .
the selection criteria were as follows : ( 1 ) the abstract and the article itself were published in a reviewed journal in english ; ( 2 ) the topic was exclusively concerned with agility or cods in sport ; ( 3 ) the aim of the study was to explain or improve agility performance , describe the agility structure or recognize the main factors determining agility ; and ( 4 ) the participants of the research studies were recreational , collegiate , amateur or professional athletes . a team of independent evaluators was appointed for full - text screening to choose relevant research studies and hand search through the reference lists of articles and books .
each evaluator was tasked with reviewing the articles and making a decision on accepting the articles based on the following inclusion criteria : ( 1 ) the number of study participants reached the minimal biological sample ( 30 people ) and ( 2 ) the agility factorial structure or its sub - factors were evaluated by either pca , factor analysis or sem .
the database search resulted in 2260 studies after removing duplicates during title and abstract screening .
a total of 60 papers were followed up by selection criteria for full - text screening ( figure 2 ) .
finally , four research studies that focused on cods met the defined inclusion criteria . using a desired methodological approach ,
no quantitative empirical research was found that attempted to verify the quality of the whole suggested model of the main factors determining agility using the sem approach or confirmatory factor analysis . during full - text screening of numerous studies concerning agility ,
factorial models were rejected because they reported only correlation coefficients across different tests and did not apply appropriate statistical methods . moreover ,
these studies had already been summarized in previous reviews ( sheppard et al . , 2014 ; young et al . , 2015 ) .
( metiko et al . , 2003 ) was rejected because it was not available in english .
additionally , other studies that used multiple regression ( mr ) were excluded ( delextrat et al .
although mr principles are directly linked to the sem approach , its core is in analyzing the observed variables only ( tabachnick et al . , 2001 ) .
therefore , mr does not evaluate relationships among multiple dependent factors ( tests ) and can not recognize the level and strength of their dependency in structural models simultaneously ( muthn and muthn , 2010 ) .
three of the studies used pca with kaiser - guttman criterion and one study used factor analysis ( exploratory approach ) ( efa ) along with regression analysis .
articles evaluating the agility factorial structure or change of direction cods = change of direction speed , * physical education student , 1rm = one repetition maximum , ssc = stretch shortening cycle , cj = countermovement jump , slj = standing long jump , sj = squat jump , dj = drop jump , ls = lateral stepping , sq = squat , sbf = sprint with backward and forward running , pca = principal component analysis , efa = factor analysis - exploratory 
 approach
only 4 studies out of 60 relevant titles discussing agility involved research attempting to determine the main factorial model of agility , which means that the current understanding of agility is based strongly on the empirical approach rather than a quantified hierarchy of manifest variables or tests .
this result seems to be appropriate because the concept of agility arose from empirical experiences and practices .
moreover , it must be emphasized that three of the studies used a pca approach .
although pca belongs to the family of exploratory techniques , it is not based on a common factor model ( fabrigar et al . , 1999 ) .
rather , pca is focused on the amount of variance explained by manifest variables instead of an explanation of the correlations between them .
therefore , pca is appropriate as a data reduction technique , but not for verifying relationships between latent variables within an evaluated structure .
pca is used mainly when it is necessary to reduce a large number of indicators to a smaller and more manageable amount of directly measured tests for subsequent analysis . in our review
, we found only one study ( markovic et al . , 2007 ) that used a common factor model from sem , specifically the exploratory factor analyses ( efa ) approach .
however , no research has verified the structure of agility using sem , such as confirmatory factor analysis or structural regression models , although many methodologists ( mcdonald , 1999 ; muthn and muthn , 2010 ) showed that approaches from a family of sem enable researchers to specify relationships between latent variables ( constructs ) and a set of measures as well as to determine the relationships between constructs .
consequently , a large number of studies described the internal relationship between agility tests , which is the relationship between a dependent variable and defined independent variables .
certainly this situation gives an apparent chance for developing new agility tests , which will probably provide a sufficient correlation with previously accepted tests .
however , adding or removing different tests can not explain the basic principles that make up agility performance .
therefore , it would be highly desirable to verify and establish a factorial model of agility by second generation data analysis techniques ( bagozzi and fornell , 1982 ) to which sem belongs . despite the theoretical background ,
the original empirical model of agility ( compounding from perceptual and decision - making skills and change - of - direction speed ) might seem to be appropriate , but its usefulness is diminished by the large number of agility test choices that do not have a quantified hierarchy .
for example , it has been reported that cods is often used in team sports ; however , performance in sports or activities that involve cods does not predict ( perhaps not with a strong relationship ) complex agility performance ( henry et al . , 2011 ; serpell et al .
one of the results from the present study is that only tests of cods and not complex agility have been analyzed to determine the variance in cods performance .
( 2014 ) concluded that common cods tests ( illinois , l - run , pro - agility , t - test , and 505 agility ) were highly reliable and valid measures of cods in collegiate athletes performing in invasive games and could explain 89.5% of the cods latent variable ( factor ) variance .
therefore , performance in these tests is a suitable and sensitive assessment of cods . as a result
, the above mentioned specific combination of selected cods tests is sufficient to assess the cods performance of athletes at the collegiate level who perform in different invasive games . on the other hand , if elite athletes representing one sport discipline are tested , the need for specific cods tests increases .
( 2010 ) showed that sprinting with backward and forward running had greater factorial validity for cods than a slalom test , a cods sprint 4 x 5 m , a t - test and a sprint with 90 or 180 turns in elite soccer players , but those tests could explain only 57% of cod variance ( table 1 ) .
these findings support the empirical knowledge that higher performance levels require more specific tests of agility involving cods . furthermore , in the future , the structure of the specific agility tests for elite athletes should be estimated independently from general agility tests .
other studies have confirmed that within the agility structure , there is a cods independent factor ( markovi et al .
, 2007 ; salaj and markovic , 2011 ; zajac et al . , 2014 ) .
( 2007 ) revealed three other independent factors ( in addition to cods ) , namely , explosive strength , elastic strength and maximum strength .
however , according to their results , these factors explained only 17% of cods performance .
later , salaj and markovic ( 2011 ) found that other independent factors could be determined , namely , sprinting ability , concentric / slow stretch and shortening cycle jumping ability and fast stretch and shortening cycle jumping ability , which together with cods explained 80% of the test variance .
these results indicated that sprinting along with jumping ability could better determine cods than explosive and maximum strength in collegiate athletes .
considering the agility concept , we can estimate that only cods and perhaps some of the technical and physical determinants have been appropriately analyzed in current literature ( figure 2 ) . on the other hand ,
some studies have focused on creating complex agility tests that also include cognitive or decision - making aspects ( serpell et al . , 2011 ; czuba et al . , 2013 ; spiteri et al . , 2014a ) ;
currently , there is poor quantitative evidence validating the previously established factorial model of agility .
only four studies used exploratory techniques such as pca or efa , and no study used structural equation modeling or confirmatory approaches for determining the relationships between indicators and concepts within the agility factor structure . therefore , we strongly suggest that future research should verify and establish a factorial agility model using a second generation data analysis technique to which the family of structural equation modelling ( sem ) belongs . from the whole structure of agility ,
the combination of common cods tests is reliable and useful to estimate cods performance in subelite athletes ; however , for elite athletes , specific cods tests that are relevant to the needs of particular sport disciplines must be constructed .
sprinting and jumping tests are stronger factors for cods than explosive and maximal strength tests .
future research should focus on the following aspects : 
 performing factor analyses of the agility factor structure for each agility classification.identification of independent factors for general and sport - specific agility tests.identification of sport - specific agility tests for elite athletes.analysis of the factorial strength of complex agility tests such as the test of reactive agility . 
 
performing factor analyses of the agility factor structure for each agility classification . identification of independent factors for general and sport - specific agility tests .
analysis of the factorial strength of complex agility tests such as the test of reactive agility .
 abstractwhile tests of basic motor abilities such as speed , maximum strength or endurance are well recognized , testing of complex motor functions such as agility remains unresolved in current literature . 
 therefore , the aim of this review was to evaluate which main factor or factor structures quantitatively determine agility . in methodological detail , this review focused on research that explained or described the relationships between latent variables in a factorial model of agility using approaches such as principal component analysis , factor analysis and structural equation modeling . 
 four research studies met the defined inclusion criteria . 
 no quantitative empirical research was found that tried to verify the quality of the whole suggested model of the main factors determining agility through the use of a structural equation modeling ( sem ) approach or a confirmatory factor analysis . from the whole structure of agility , 
 only change of direction speed ( cods ) and some of its subtests were appropriately analyzed . 
 the combination of common cods tests is reliable and useful to estimate performance in sub - elite athletes ; however , for elite athletes , cods tests must be specific to the needs of a particular sport discipline . 
 sprinting and jumping tests are stronger factors for cods than explosive strength and maximum strength tests . 
 the authors suggest the need to verify the agility factorial model by a second generation data analysis technique such as sem . 
Introduction
Material and Methods
Results
Discussion
Conclusion