## Observing Interaction: An Introduction to Sequential AnalysisMothers and infants exchanging gleeful vocalizations, married couples discussing their problems, children playing, birds courting, and monkeys fighting all have this in common: their interactions unfold over time. Almost anyone who is interested can observe and describe such phenomena. However, scientists usually demand more than a desription--they want observations that are replicable and amenable to scientific analysis, while still faithful to the dynamics of the phenomena studied. This book provides a straightforward introduction to scientific methods for observing social behavior. The second edition clarifies and extends material from the first edition, especially with respect to data analysis. A common standard for sequential data is introduced and sequential analysis is placed on firmer, log-linear statistical footing. The second edition is designed to work as a companion volume to Analyzing Interaction (1995). Because of the importance of time in the dynamics of social interaction, sequential approaches to analyzing and understanding social behavior are emphasized. An advanced knowledge of statistical analysis is not required. Instead, the authors present fundamental concepts and offer practical advice. |

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### Contents

Introduction | 1 |

12 Alternatives to systematic observation | 2 |

13 Systematic observation defined | 3 |

Partens study of childrens play | 4 |

15 Social process and sequential analysis | 6 |

Smiths study of parallel play | 7 |

Bakeman and Brownlees study of parallel play | 8 |

18 Hypothesisgenerating research | 12 |

53 State sequences | 83 |

54 Timedevent sequences | 84 |

55 Interval sequences | 85 |

56 Crossclassified events | 87 |

57 Transforming representations | 88 |

58 Summary | 90 |

Analyzing sequential data First steps | 91 |

62 Rates and frequencies | 92 |

Systematic is not always sequential | 13 |

Developing a coding scheme | 15 |

22 What is the question? | 16 |

23 Physically versus socially based coding schemes | 17 |

24 Detectors versus informants | 22 |

26 Keeping it simple | 23 |

27 Splitting and lumping | 24 |

28 Mutually exclusive and exhaustive codes | 26 |

29 The evolution of a coding system | 27 |

Interaction of prehatched chickens | 28 |

Childrens conversations | 30 |

Baby behavior codes | 33 |

Childrens object struggles | 34 |

Monkeys activities | 35 |

215 Summary | 36 |

Recording behavioral sequences | 38 |

33 Continuous versus intermittent recording | 39 |

34 Coding events | 40 |

35 Recording onset and offset times | 43 |

36 Timing pattern changes | 45 |

37 Coding intervals | 46 |

38 Crossclassifying events | 49 |

Time sampling | 50 |

310 The pleasures of pencil and paper | 52 |

312 Summary | 55 |

Assessing observer agreement | 56 |

42 Reliability versus agreement | 59 |

43 The problem with agreement percentages | 60 |

44 The advantages of Cohens kappa | 62 |

45 Agreement about unitizing | 68 |

Examples using Cohens kappa | 71 |

47 Generalizability theory | 75 |

48 Unreliability as a research variable | 79 |

49 Summary | 80 |

Representing observational data | 81 |

52 Event sequences | 82 |

63 Probabilities and percentages | 93 |

64 Mean event durations | 94 |

An introduction | 95 |

66 Summary | 99 |

Analyzing event sequences | 100 |

72 Determining significance of particular chains | 101 |

73 Transitional probabilities revisited | 103 |

74 Computing z scores and testing significance | 108 |

75 Classic lag sequential methods | 111 |

76 Loglinear approaches to lagsequential analysis | 116 |

77 Computing Yules Q or phi and testing for individual differences | 127 |

78 Summary | 132 |

Issues in sequential analysis | 136 |

82 Stationarity | 138 |

83 Describing general orderliness | 139 |

84 Individual versus pooled data | 141 |

85 How many data points are enough? | 144 |

86 The type I error problem | 147 |

87 Summary | 148 |

Analyzing time sequences | 150 |

92 Taking time into account | 151 |

93 Micro to macro | 153 |

94 Timeseries analysis | 154 |

95 Autocorrelation and timeseries analysis | 167 |

96 Summary | 175 |

Analyzing crossclassified events | 177 |

A simple example | 179 |

103 Summary | 182 |

Epilogue | 184 |

112 Soskin and John on marital interaction | 185 |

113 Marital interaction research since 1963 | 188 |

A Pascal program to compute kappa and weighted kappa | 194 |

198 | |

205 | |

### Other editions - View all

Observing Interaction: An Introduction to Sequential Analysis J. M. Gottman,R. Bakeman No preview available - 1986 |

Observing Interaction: An Introduction to Sequential Analysis Roger Bakeman,John M. Gottman No preview available - 1997 |

### Common terms and phrases

analyzed assume autocorrelation Bakeman & Quera Bakeman and Brownlee behavioral codes Blook cells chapter chi-square child codable events coded events coders codes cannot repeat codes may repeat coding system Cohen's kappa computed conditional independence conditional probability consecutive codes couples cross-classifying events defined described disagreement discussed event sequences example exclusive and exhaustive expected frequencies expected values Figure given Gottman infant interval sequences investigators John Gottman Log-linear models marital interaction married couples methods mother mutually exclusive negative affect number of tallies observed frequencies occurred odds ratio onset and offset parallel play Parten percentage prior possession questions recording strategy reliability represented sampling saturated model scores segment sequential analysis sequential data significant simple probability socially based Solitary spectral density statistics stream of behavior structural zeros systematic observation three-event time-series analysis timed-event sequences transitional probabilities two-event sequences type I error Unoccupied variance videotapes Yule's Q