# Data analysis lecture 1-3 part 1

rename
winniesmith2's
version from
2017-10-15 15:40

## Section 1

Question | Answer |
---|---|

An experimental research design is a balance between what (5 points) | 1. Appropriate Sample and Sample Size 2. Accurate/appropriate variables to reduce error 3. Validity of the measuring instrument(s) 4. Practicality of conducting the experiment 5. Costs/Budgets |

Scale variables are | continuous variables |

Values within a range or a variable that only take a finite number of values | discrete variables |

Values that the variable can take are categories or categorical | categorical variables |

Levels of measurement; interval | This allows us to put scores in some sort of order of magnitude and we also have equal intervals between adjacent points on the scale. e.g. temperature |

Levels of measurement; ratio | Ratio scales have all the features of interval-level data but with the addition of having an absolute zero point, e.g. timing a Formula 1 race |

Levels of measurement; nominal | An example of a nominal scale is gender - (male/female). Put people into categories and the data we obtain are in the form of frequency counts (e.g. ethnicity). |

Levels of measurement; ordinal | Ratings scales to measure participants’ responses, e.g. measure how nervous a student is before taking an exam by using a scale |

What are extraneous variables | are those variables that might have an impact on the other variables that we are interested in but we may have failed to take these into account when designing our study |

What are confounding variables | is a specific type of extraneous variable that is related to both of the main variables that we are interested in. |

## Section 2

Question | Answer |
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3 types of experimental primary research designs | correlational, experimental, quasi experimental. |

What is a correlational research design | A design where we measure the variables of interest and then see how each variable changes in relation to the changes in the other variables.those that investigate relationships (or measures of association) between variables. The sorts of statistical technique we will use for correlational design are the Pearson product moment correlation coefficient, or perhaps Spearman’s rho correlation coefficient, simple and multiple linear regression. |

Problems with correlational research design | Causation. Researchers are trying to discover causal relationships between variables. In a correlational designs, however, it is difficult to establish whether a change in one variable causes a change in another variable. The reason for this is that in such designs we are simply observing and recording changes in variables and trying to establish whether they co-vary in some meaningful way. One of the golden rules of correlational designs is that we cannot infer causation from correlations. |

What is a experimental research designs | one of the most widely used designs in science. Experimental designs are those where the experimenter manipulates one variable called the Independent Variable (IV) to see what effect this has upon another variable called the Dependent Variable (DV). In experimental designs we are usually looking for differences between conditions of the IV. Uses research hypothesis and participants and randomly allocated into conditions. |

What is a research hypothesis | our prediction of how specific variables might be related to one another or how groups of participants might be different from each other. |

What is a quasi experimental design | involve seeing if there are differences on the dependent variable (DV) between conditions of the independent variable (IV). Unlike experimental designs there is not random allocation of participants to the various conditions of the IV. |

What statistical technique do you use for experimental or quasi-experimental design | are the t-test (also known as the Student t-test), the Mann–Whitney U test, the Wilcoxon test and ANalysis Of VAriance (ANOVA |

Structure of an experimental design | obs 1 (Timing 1) - exp - obs 2(T2) at the same time as Obs 3 (T1) - No exp - Obs 4(T2). The difference between each groups pre and post test scores is then analysed to establish whether or not Exp has made a difference. |

## Section 3

Question | Answer |
---|---|

What is a withing-participants design | have the same participants in every condition of the independent variable (IV). Each participant performs under all conditions in the study. Sometimes called repeated measures or related design. |

What is a between-participants design | (also sometimes known as an independent or unrelated design),have different groups of participants in each condition of the independent variable (IV). Thus, the group of participants in one condition of the IV is different from the participants in another condition of the IV. |

Advantages of within-participants design | -You can control the many inter-individual confound variables. -You need to find fewer participants – low cost, e.g. each participant experiences two or more conditions. |

Disadvantages of within-participant design | People get bored or tired could lead to introducing a confound called order effect.Participant realise the purpose of the experiment and conform to the experiment – demand effect. |

What are order effects | a consequence of within-participant designs whereby completing the conditions in a particular order leads to differences in the dependent variable that are not a result of the manipulation of the independent variable (IV). Differences between the conditions of the IV might be due to practice, fatigue or boredom rather than to the experimenter’s manipulation of the IV. Effects can be overcome/reduced by counterbalancing |

What is counterbalancing | where you systematically vary the order in which participants take part in the various conditions of the independent variable (IV). Counterbalancing would be introduced into a study where you have a within-participants design. |

Advantages of between participant design | Participant is less likely to get bored or tired between-participants designs therefore it reduces order and demand effects |

Disadvantages of between participant design | -Need more participants. -Lose certain degrees of control over any confounding variables. -One of the problems of participants designs is that different people bring different characteristics to the experimental setting. When we are randomly allocating participants to conditions, we might, by chance, allocate all participants with one characteristic to one group, and this might confound our results. |

What is a cross sectional design | often called “Social Survey Design". Researchers are interested in variation. This can only be established when one or more cases are examined. Data on the variables of interest is collected at a single point in time The design allows the examination of relationship between variables – i.e. patterns of association but not causes. “A cross sectional design entails the collection of data on more than one case and at a single point in time in order to collect a body of quantitative or quantifiable data in connection with two or more variables which are then examined to detect patterns of association”. (Bryman and Bell, 2011). |

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