Understanding Games for Teaching Reflections on Empirical Approaches in Team Sports Research

Prof. Dr. Stefan König Understanding Games for Teaching Reflections on Empirical Approaches in Team Sports Research Lecture on the 10 th dvs Sportspiel- Symposium meets 6 th International TGfU Conference on July 25 th, 2016 in Cologne Cologne, July 25th 2016 Prof. Dr. Stefan König Folie 1

Four preliminary remarks First, my lecture focuses PE and game instruction at school, i. e. it deals with intact groups and individuals having different attitudes, motivation, expertise, and experience. Second and as to PE, I believe in the idea of the double mission, i. e. the idea of interlinking qualification and personal development within one strand of education. Third, and as a consequence, I feel obliged to follow this idea even in my research approaches. Finally, this lecture focusses Reflections on and not Innovations in, thus Cologne, July 25th, 2016 Prof. Dr. Stefan König 3

Agenda A Framework for Teaching in PE From Monomethod Experimental to Multilevel Mixed Evaluation Studies Summary: MMR in RT-PE Questions and Discussion Cologne, July 25th, 2016 Prof. Dr. Stefan König 5

A Framework for Teaching in PE Cologne, July 25th, 2016 Prof. Dr. Stefan König 6

Helmke s Model of instruction and learning Individual Preconditions Personality and Expertise of Teacher Instruction Mediation Processes Learning Activities Effects Context of class and subject / topic From: Helmke (2007, p. 42) Cologne, July 25th, 2016 Prof. Dr. Stefan König 7

Educational objectives in PE and Game Instruction (Kolb, 2005, pp. 68 71) Sport socialization Objective conditions Sport as a rulegoverned system Movement personalization Subjective options and desires Humane potentials Games as rulegoverned systems e. g. skill-orientated instruction Homo ludens or filius ludens e. g. genetic learning Cologne, July 25th, 2016 Prof. Dr. Stefan König 9

Intermediate results PE aims at a physically educated person, i. e. the interlinking qualification as well as personalization; consequently, educational aims are rather complex. This seems to apply in particular for team sports, because students need to be educated on both an individual and a team level. As a consequence my initial theses have the following wording: (1)To understand games for teaching we should have a new debate on methodological issues. (2)Within this debate, I believe we should gain multi and mixed methods research center stage. Cologne, July 25th, 2016 Prof. Dr. Stefan König 10

From Monomethod Experimental to Multilevel Mixed Evaluation Studies Cologne, July 25th, 2016 Prof. Dr. Stefan König 11

Key Features of the Study Sample size n = 325 Class 6 8 Facts and Figures Age Mean: 12,59 (11 to 15, SD:.954) Treatment Soccer, Team Handball, Volleyball Measurement Pre-Post-Design (2) Data collection and analysis Expert rating (marks) Questionnaires (Stu) Semi-structured interviews (T) Monomethod Approach Nested Data Approach Mixed Approach Cologne, July 25th, 2016 Prof. Dr. Stefan König 12

Step 1: Monomethod Approach: ANCOVA Procedures Cologne, July 25th, 2016 Prof. Dr. Stefan König 13

Research Questions Which method proves itself as most effective? Which factors help to explain withingroup differences? Which method gives reason to expect the most sustainable results? Cologne, July 25th, 2016 Prof. Dr. Stefan König 14

1 Group (1) 2 Individual 3 Individual and predictor Group 4 Teachers 5 Students Cologne, July 25th, 2016 Prof. Dr. Stefan König 15

1 Group (2) 2 Individual 3 Individual and predictor Group 4 Teachers 5 Students Main effect learning : F = 169,984, p =.000 η 2 = 0.386 Interaction effect learning*method*game : F = 18,139, p =.000 η 2 = 0.122 Greatest effect method : TGfU (d = 0.46, CI: 0.21 0.69) Greatest effect game : Volleyball (d = 3,77, CI: 2,71 4,84) (!) Cologne, July 25th, 2016 Prof. Dr. Stefan König 16

1 Group (3) 2 Individual 3 Individual and predictor Group 4 Teachers 5 Students Are there differences in the individual trajectories? Which predictors help to explain such within-group differences? Are there differences between classes or schools? Cologne, July 25th, 2016 Prof. Dr. Stefan König 17

1 Group (3) 2 Individual 3 Individual and predictor Group 4 Teachers 5 Students Are there differences in the individual trajectories? Which predictors help to explain such within-group differences? Are there differences between classes or schools? Cologne, July 25th, 2016 Prof. Dr. Stefan König 18

Step 2: Nested Data Approach: Multilevel Regression Models Cologne, July 25th, 2016 Prof. Dr. Stefan König 19

Multilevel Approach in RT-PE Level 3 Groups Group characteristics Level 2 Individual i Robust individual characteristics Level 1 P 0 P 0. P n Level-3: Groups (Snijders & Bosker, 2010, p. 247) Level-2: Robust individual characteristics (Singer & Willett, 2003, pp. 57 63) Level-1: Measurement points (Luke, 2004, p. 62 64) Cologne, July 25th, 2016 Prof. Dr. Stefan König 20

ABILITY ABILITY ABILITY ABILITY ABILITY ABILITY ABILITY ABILITY ABILITY ABILITY ABILITY ABILITY ABILITY ABILITY ABILITY ABILITY 1 Group 2 Individual 3 Individual and predictor Group 4 Teachers 5 Students 6.24 4.91 3.58 2.25 Lev -id 3 6.24 4.91 3.58 2.25 0.92 Lev -id 12 0.92 6.24 4.91 3.58 2.25 0.92 6.24 4.91 3.58 2.25 Lev -id 10 Lev -id 13 0.92 6.24 4.91 3.58 2.25 Lev -id 51 6.24 4.91 3.58 2.25 0.92 Lev -id 56 0.92 6.24 4.91 3.58 2.25 Lev -id 54 6.24 4.91 3.58 2.25 0.92 Lev -id 64 0.92 6.24 4.91 3.58 2.25 Lev -id 26 0.92 6.24 4.91 3.58 2.25 Lev -id 28 0.92 6.24 4.91 3.58 2.25 Lev -id 69 0.92 6.24 4.91 3.58 2.25 Lev -id 92 0.92 6.24 4.91 3.58 2.25 Lev -id 30 0.92 6.24 4.91 3.58 2.25 Lev -id 32 0.92 6.24 4.91 3.58 2.25 Lev -id 104 0.92 6.24 4.91 3.58 2.25 Lev -id 111 0.92 Cologne, July 25th, 2016 Prof. Dr. Stefan König 21

ABILITY 1 Group 2 Individual 3 Individual & predictor Group (1) 4 Teachers 5 Students 6.20 TGFU = 0 TGFU = 1 5.10 4.00 2.90 1.80 Cologne, July 25th, 2016 Prof. Dr. Stefan König 22

1 Group 2 Individual 3 Individual & predictor Group (2) 4 Teachers 5 Students A two level hierarchical regression model Level-1 Model (1) ABILITY ti = π 0i + π 1i *( ti ) + e ti Level-2 Model (2) π 0i = β 00 + β 01 *(MALE i ) + r 0i (3) π 1i = β 10 + β 11 *(MALE i ) + r 1j Mixed Model (4) ABILITY ti = β 00 + β 01 *MALE i + β 10 * ti + β 11 *MALE i * ti + r 0i + r 1j * + e ti Cologne, July 25th, 2016 Prof. Dr. Stefan König 23

1 Group 2 Individual 3 Individual & predictor Group (3) 4 Teachers 5 Students Fixed Effect Coefficient Standard error For INTRCEPT1, π 0 (starting value of individual i at time = 0 [baseline]) For INTRCEPT2, β 00 (average initial status for level 2 = 1 [male]) t-ratio Approx. d.f. p-value INTRCEPT3, γ 000-1.423216 0.203483-6.994 4 0.002 TGfU, γ 001 0.495575 0.290333 1.707 4 0.163 For GENDER, β 01 (difference in initial status between female and male participants) INTRCEPT3, γ 010 1.076207 0.134735 7.988 137 <0.001 TGfU, γ 011-0.481766 0.208632-2.309 137 0.022 For slope, π 1 (rate of change of individual i [effect]) For INTRCEPT2, β 10 (average change for level 2 = 1 [male]) INTRCEPT3, γ 100-0.193473 0.074259-2.605 137 0.010 TGfU, γ 101 0.517990 0.106011 4.886 137 <0.001 For GENDER, β 11 (difference in change between female and male) INTRCEPT3, γ 110 0.115913 0.049179 2.357 137 0.020 TGfU, γ 111-0.318435 0.076205-4.179 137 <0.001 Cologne, July 25th, 2016 Prof. Dr. Stefan König 24

1 Group 2 Individual 3 Individual & predictor Group (5) 4 Teachers 5 Students Why do students having been exposed to TGfU lessons improve better? Why do students of the non-invasion classes improve better than others? Why do female students show higher than males? Cologne, July 25th, 2016 Prof. Dr. Stefan König 25

Step 3: Integrating QUAL + QUAN Data: Multilevel Multimethod Evaluation Studies Cologne, July 25th, 2016 Prof. Dr. Stefan König 26

QUAL: Data collection via interviews (T) and questionnaires (S) to explain within- and between group differences on level 2 Level 2 Individuum i Predictors: gender program game Level 1 P 0 P 0. P n QUAN: Data collection via expert rating to understand and explain of predictors Cologne, July 25th, 2016 Prof. Dr. Stefan König 27

Additional strands Teachers perspective QUAL: Guided Interviews immediately after the intervention, but without information about the results of the output study N = 8 Transcription due to GAT Content analysis Students perspective QUAL / QUAN: Questionnaires with open / closed questions immediately after the intervention but without any information about the output study N = 65 Analysis via descriptives and inferential procedures Cologne, July 25th, 2016 Prof. Dr. Stefan König 28

1 Group 2 Individual 3 Individual and predictor Group 4 Teachers 5 Students Category TGfU Progress in game ability requires deliberate play and deliberate exercise as well as a reasonable interlinking of the two within a circular model on different levels of time. Category Game Especially volleyball requires this connection; as a consequence teachers believe that TGfU works more effective in the volleyball classes. However, the baseline average was rather low in the volleyball classes. Category Gender For me an addition of playing and deliberate exercise is inevitable in girls classes, because they actually do not have the same experiences as boys. Cologne, July 25th, 2016 Prof. Dr. Stefan König 29

1 Group 2 Individual 3 Individual and predictor Group 4 Teachers 5 Students Preliminary note: Data from students have only been analyzed for a small part of the sample; thus, we cannot comment on gender as one of the quantitative predictors. Category Program Students insist on great amounts of playing, thus accepting TGfU and Playing. They prefer explicit teaching of tactical elements, thus favoring TGfU programs. Interpretation: Higher of TGfU may be explained hereby Category Game+Method It is the person of the teacher who influences the acceptance of liking or disliking a game and a method. Interpretation: Different may be explained with the personality of the teacher. Cologne, July 25th, 2016 Prof. Dr. Stefan König 30

Summary: MMR in RT-PE Cologne, July 25th, 2016 Prof. Dr. Stefan König 31

Evaluation of a QUAN + QUAN + QUAL-Study QUAN 1 QUAN 2 QUAL 1 Data collection Expert rating Interviews Unit of analysis Individual at level 1 Individual at level 2 Individual at level 2 Data analysis Description + regression analysis HLM 2 (with repeated measures on level 1) Content analysis Mixing --- QUAN 1 + QUAN 2 QUAN 1 + QUAN 2 + QUAL 1 Rationale Overview Nested Data Describing and explaining deviance Limitation Diversity is not described Within- and between group deviances cannot be explained Deviances can be explained, but the results are only valid for the sample Cologne, July 25th, 2016 Prof. Dr. Stefan König 32

Rationales for using MMR in RT-PE Triangulation Complementarity Precision Instrument Development Initiation Expansion Cologne, July 25th, 2016 Prof. Dr. Stefan König 33

Possibilities MMR may help to gain more insight into the process of inquiring processes, social dynamics and outcomes (motor skill, attitude, knowledge and fitness) of physical education. Greater depth and breadth, because MMR leads to Precision Enhancement Contradiction Limitations As to the use of rigorous methods MMR often needs a team of researchers. Pragmatism, the underlying world-view, does not satisfy everybody s epistemological expectations. MMR needs more time, more money, Due to small samples this is dramatic, because of the necessity of replication studies. Cologne, July 25th, 2016 Prof. Dr. Stefan König 34

Thank you very much for your attention! www.ph-weingarten.de Cologne, July 25th 2016 Prof. Dr. Stefan König Folie 35

Questions and Discussion Cologne, July 25th, 2016 Prof. Dr. Stefan König 36